Software Design Document: Voice-Centric Software Development and Data Science Agent

System: A sophisticated AI agent system that leverages voice interfaces to facilitate software development and data science workflows. The system enables developers and data scientists to interact through natural language voice commands to design architectures, write code, analyze data, manage projects, and collaborate on technical tasks. The agent integrates with development tools, data science platforms, and project management systems to provide a seamless voice-driven development experience.

Generated: 2025-11-26 13:36:55

Use Cases & Actors

Voice-Centric Software Development and Data Science Agent

Comprehensive Use Case Documentation

1. ACTOR IDENTIFICATION

1.1 Primary Actors

Actor: Software Developer (DEV)

Role: Primary user who develops software applications using voice commands Goals:

• Design software architectures through voice interaction
• Write, review, and refactor code efficiently
• Manage version control operations
• Debug applications with voice assistance
• Collaborate with team members on code

Characteristics:

• Proficiency in multiple programming languages
• Familiar with development tools and IDEs
• Requires low-latency voice processing
• Works across multiple projects simultaneously

Actor: Data Scientist (DS)

Role: Analyzes data and builds machine learning models using voice interface Goals:

• Explore and analyze datasets through voice commands
• Build and train machine learning models
• Visualize data insights
• Manage data pipelines
• Document analysis findings

Characteristics:

• Expertise in statistical analysis and ML frameworks
• Works with large datasets
• Requires integration with Jupyter notebooks and data tools
• Needs reproducible analysis workflows

Actor: Project Manager (PM)

Role: Oversees project execution and team coordination Goals:

• Track project progress and milestones
• Manage team assignments and workload
• Monitor project risks and issues
• Generate project reports
• Facilitate team communication

Characteristics:

• Non-technical or semi-technical background
• Requires high-level project visibility
• Needs real-time status updates
• Focuses on timeline and resource management

Actor: DevOps Engineer (DEVOPS)

Role: Manages infrastructure, deployment, and system operations Goals:

• Deploy applications to various environments
• Monitor system performance and health
• Manage CI/CD pipelines
• Configure infrastructure as code
• Handle incident response

Characteristics:

• Deep infrastructure and automation knowledge
• Requires integration with deployment tools
• Needs real-time monitoring capabilities
• Focuses on system reliability and performance

Actor: Technical Architect (ARCH)

Role: Designs system architecture and technical solutions Goals:

• Design scalable system architectures
• Define technical standards and best practices
• Review architectural decisions
• Mentor development teams
• Evaluate technology choices

Characteristics:

• Extensive technical experience
• Requires high-level system design capabilities
• Needs access to architectural documentation
• Focuses on long-term technical strategy

Actor: Security Officer (SEC)

Role: Ensures security compliance and data protection Goals:

• Audit code for security vulnerabilities
• Manage access control and permissions
• Monitor security incidents
• Ensure compliance with regulations
• Review security policies

Characteristics:

• Security and compliance expertise
• Requires audit trail access
• Needs integration with security tools
• Focuses on risk mitigation

1.2 Secondary Actors (External Systems)

External System: IDE Integration Service (IDE-SVC)

Role: Provides integration with development environments Responsibilities:

• Receive code generation requests
• Execute code snippets
• Provide IDE state information
• Manage file operations

External System: Git Repository Service (GIT-SVC)

Role: Manages version control operations Responsibilities:

• Handle commit, push, pull operations
• Manage branches and merge requests
• Provide repository information
• Track change history

External System: Jupyter Notebook Service (JUPYTER-SVC)

Role: Manages data science notebooks Responsibilities:

• Execute notebook cells
• Manage notebook state
• Generate visualizations
• Export analysis results

External System: CI/CD Pipeline Service (CICD-SVC)

Role: Manages continuous integration and deployment Responsibilities:

• Trigger build pipelines
• Execute tests
• Deploy applications
• Provide pipeline status

External System: Monitoring and Analytics Service (MONITOR-SVC)

Role: Provides system monitoring and analytics Responsibilities:

• Collect performance metrics
• Generate alerts
• Provide system health status
• Track resource utilization

External System: Authentication Service (AUTH-SVC)

Role: Manages user authentication and authorization Responsibilities:

• Authenticate users
• Manage access tokens
• Enforce permission policies
• Audit access logs

External System: Voice Processing Engine (VOICE-ENGINE)

Role: Processes voice input and generates voice output Responsibilities:

• Convert speech to text (STT)
• Convert text to speech (TTS)
• Perform voice recognition
• Handle voice quality optimization

External System: Natural Language Processing Service (NLP-SVC)

Role: Processes and understands natural language commands Responsibilities:

• Parse voice commands
• Extract intent and entities
• Resolve ambiguities
• Generate contextual responses

External System: Project Management Service (PM-SVC)

Role: Manages project data and workflows Responsibilities:

• Store project information
• Manage tasks and assignments
• Track milestones
• Generate reports

External System: Data Storage Service (STORAGE-SVC)

Role: Manages persistent data storage Responsibilities:

• Store user data and preferences
• Manage file storage
• Provide data retrieval
• Handle backup and recovery

1.3 Actor Relationships

graph TD
    DEV["Software Developer"]
    DS["Data Scientist"]
    PM["Project Manager"]
    DEVOPS["DevOps Engineer"]
    ARCH["Technical Architect"]
    SEC["Security Officer"]
    
    VOICE["Voice Processing Engine"]
    NLP["NLP Service"]
    IDE["IDE Integration"]
    GIT["Git Repository"]
    JUPYTER["Jupyter Notebook"]
    CICD["CI/CD Pipeline"]
    MONITOR["Monitoring Service"]
    AUTH["Authentication"]
    PM_SVC["Project Management"]
    STORAGE["Data Storage"]
    
    DEV -->|Uses| VOICE
    DS -->|Uses| VOICE
    PM -->|Uses| VOICE
    DEVOPS -->|Uses| VOICE
    ARCH -->|Uses| VOICE
    SEC -->|Uses| VOICE
    
    VOICE -->|Processes| NLP
    NLP -->|Integrates| IDE
    NLP -->|Integrates| GIT
    NLP -->|Integrates| JUPYTER
    NLP -->|Integrates| CICD
    NLP -->|Integrates| MONITOR
    NLP -->|Integrates| PM_SVC
    
    AUTH -->|Authenticates| DEV
    AUTH -->|Authenticates| DS
    AUTH -->|Authenticates| PM
    AUTH -->|Authenticates| DEVOPS
    AUTH -->|Authenticates| ARCH
    AUTH -->|Authenticates| SEC
    
    STORAGE -->|Stores| IDE
    STORAGE -->|Stores| GIT
    STORAGE -->|Stores| JUPYTER
    STORAGE -->|Stores| PM_SVC
    
    SEC -->|Audits| AUTH
    SEC -->|Monitors| STORAGE
    
    ARCH -->|Designs| IDE
    ARCH -->|Designs| CICD
    ARCH -->|Designs| MONITOR
    
    PM -->|Manages| DEV
    PM -->|Manages| DS
    PM -->|Manages| DEVOPS

2. USE CASE CATALOG

UC-001: Process Voice Command

UC-ID: UC-001
Name: Process Voice Command
Primary Actor: Software Developer, Data Scientist, Project Manager, DevOps Engineer, Technical Architect, Security Officer
Secondary Actors: Voice Processing Engine, NLP Service, Authentication Service

Preconditions:

• User is authenticated and authorized
• Voice processing engine is operational
• NLP service is available
• User has active voice session

Main Success Scenario:

1. User initiates voice command by pressing voice activation button or saying wake word
2. Voice Processing Engine captures audio stream
3. Voice Processing Engine converts speech to text (STT) with >95% accuracy
4. NLP Service receives transcribed text
5. NLP Service parses command and extracts intent and entities
6. NLP Service validates command syntax and context
7. NLP Service routes command to appropriate handler service
8. Handler service executes command
9. System generates response
10. Voice Processing Engine converts response to speech (TTS)
11. System plays audio response to user
12. System logs command execution for audit trail

Alternative Flows:

AF-001-A: Speech Recognition Failure

• At step 3: If STT accuracy is <80%
  • 3a. Voice Processing Engine requests user to repeat command
  • 3b. User repeats command
  • 3c. Resume from step 3

AF-001-B: Intent Ambiguity

• At step 5: If NLP Service cannot determine intent with >85% confidence
  • 5a. NLP Service generates clarification question
  • 5b. System plays clarification question to user
  • 5c. User provides clarification
  • 5d. NLP Service re-analyzes command with additional context
  • 5e. Resume from step 6

AF-001-C: Command Validation Failure

• At step 6: If command syntax is invalid
  • 6a. NLP Service generates error message with suggestions
  • 6b. System plays error message to user
  • 6c. User can retry or cancel
  • 6d. If retry: Resume from step 1; If cancel: End use case

AF-001-D: Service Unavailability

• At step 7: If handler service is unavailable
  • 7a. System generates error message
  • 7b. System suggests alternative actions or retry time
  • 7c. System plays message to user
  • 7d. End use case

AF-001-E: Authentication Failure

• At precondition: If user is not authenticated
  • Pre-a. System requests authentication
  • Pre-b. User provides credentials
  • Pre-c. Authentication Service validates credentials
  • Pre-d. If valid: Continue with main flow; If invalid: Deny access

Postconditions:

• Command execution is logged
• User receives response (audio or visual)
• System state is updated if applicable
• Audit trail is recorded

Business Rules:

• BR-001: All voice commands must be processed within 500ms latency
• BR-002: Voice command accuracy must be ≥95%
• BR-003: All commands must be authenticated and authorized
• BR-004: Command execution must be logged for audit purposes
• BR-005: System must support context-aware command processing
• BR-006: Voice responses must be generated within 1 second
• BR-007: System must handle concurrent voice sessions from multiple users

Traceability:

• Relates to: FR-001, FR-002, FR-003, NFR-001, NFR-002

UC-002: Design Software Architecture

UC-ID: UC-002
Name: Design Software Architecture
Primary Actor: Technical Architect, Software Developer
Secondary Actors: IDE Integration Service, NLP Service, Data Storage Service

Preconditions:

• User is authenticated as Technical Architect or Senior Developer
• Project context is established
• Architecture design tools are available
• User has appropriate permissions

Main Success Scenario:

1. User initiates architecture design session with voice command
2. System loads project context and existing architecture
3. User describes architectural requirements through voice
4. NLP Service extracts architectural components and relationships
5. System generates architecture diagram based on requirements
6. System displays diagram to user for review
7. User provides feedback or modifications through voice commands
8. System updates architecture diagram
9. User requests architecture documentation generation
10. System generates comprehensive architecture documentation
11. System stores architecture design in repository
12. System notifies team members of architecture update

Alternative Flows:

AF-002-A: Architecture Validation Failure

• At step 5: If generated architecture violates design principles
  • 5a. System identifies violations and explains issues
  • 5b. System suggests improvements
  • 5c. User can accept suggestions or modify manually
  • 5d. Resume from step 5

AF-002-B: Complexity Threshold Exceeded

• At step 4: If architecture complexity exceeds threshold
  • 4a. System suggests decomposition strategies
  • 4b. User can accept suggestions or continue
  • 4c. Resume from step 5

AF-002-C: Conflicting Requirements

• At step 3: If user provides conflicting requirements
  • 3a. System identifies conflicts
  • 3b. System requests clarification
  • 3c. User resolves conflicts
  • 3d. Resume from step 4

Postconditions:

• Architecture design is documented
• Architecture diagram is generated and stored
• Team members are notified
• Architecture is available for implementation
• Design decisions are recorded

Business Rules:

• BR-008: Architecture must follow organizational standards
• BR-009: Architecture must support scalability requirements
• BR-010: Architecture must include security considerations
• BR-011: Architecture documentation must be auto-generated
• BR-012: Architecture changes must be version controlled
• BR-013: Architecture must be reviewed by Technical Architect

Traceability:

• Relates to: FR-004, FR-005, NFR-003, NFR-004

UC-003: Generate Code

UC-ID: UC-003
Name: Generate Code
Primary Actor: Software Developer
Secondary Actors: IDE Integration Service, NLP Service, Git Repository Service

Preconditions:

• User is authenticated as Software Developer
• Project is selected
• IDE is connected and ready
• Code generation templates are available
• User has write permissions to repository

Main Success Scenario:

1. User initiates code generation with voice command
2. User specifies code requirements through natural language
3. NLP Service extracts code specifications (language, framework, patterns)
4. System retrieves relevant code templates and examples
5. System generates code based on specifications
6. System displays generated code to user for review
7. User reviews code and provides feedback
8. System refines code based on feedback
9. User approves generated code
10. System inserts code into IDE at specified location
11. System formats code according to project standards
12. System commits code to Git repository with auto-generated message
13. System notifies team of code generation

Alternative Flows:

AF-003-A: Code Quality Issues

• At step 5: If generated code has quality issues
  • 5a. System identifies issues (complexity, security, performance)
  • 5b. System suggests improvements
  • 5c. User can accept suggestions or modify
  • 5d. Resume from step 5

AF-003-B: Specification Ambiguity

• At step 2: If user specifications are ambiguous
  • 2a. System asks clarifying questions
  • 2b. User provides clarifications
  • 2c. Resume from step 3

AF-003-C: Template Not Found

• At step 4: If no suitable template exists
  • 4a. System uses generic code generation
  • 4b. System generates base code structure
  • 4c. Resume from step 5

AF-003-D: Code Conflict

• At step 10: If generated code conflicts with existing code
  • 10a. System identifies conflicts
  • 10b. System suggests merge strategies
  • 10c. User resolves conflicts
  • 10d. Resume from step 10

Postconditions:

• Code is generated and inserted into IDE
• Code is committed to repository
• Code follows project standards
• Team is notified of code generation
• Code is ready for testing

Business Rules:

• BR-014: Generated code must pass linting checks
• BR-015: Generated code must follow project coding standards
• BR-016: Generated code must include appropriate comments
• BR-017: Code generation must support multiple programming languages
• BR-018: Generated code must be version controlled
• BR-019: Code generation must include security best practices

Traceability:

• Relates to: FR-006, FR-007, NFR-005, NFR-006

UC-004: Analyze Data

UC-ID: UC-004
Name: Analyze Data
Primary Actor: Data Scientist
Secondary Actors: Jupyter Notebook Service, NLP Service, Data Storage Service

Preconditions:

• User is authenticated as Data Scientist
• Dataset is available and accessible
• Jupyter notebook environment is ready
• Data analysis libraries are installed
• User has read permissions to dataset

Main Success Scenario:

1. User initiates data analysis session with voice command
2. User specifies dataset and analysis objectives through voice
3. NLP Service extracts analysis requirements (metrics, visualizations, models)
4. System loads dataset into Jupyter notebook
5. System generates exploratory data analysis (EDA) code
6. System executes EDA code and generates visualizations
7. System displays analysis results to user
8. User requests additional analysis through voice commands
9. System generates and executes analysis code
10. System displays results and insights
11. User requests statistical tests or model building
12. System generates and executes model code
13. System displays model results and performance metrics
14. User exports analysis results and documentation
15. System stores analysis notebook and results

Alternative Flows:

AF-004-A: Data Quality Issues

• At step 4: If dataset has quality issues
  • 4a. System identifies missing values, outliers, inconsistencies
  • 4b. System suggests data cleaning strategies
  • 4c. User can accept suggestions or specify custom cleaning
  • 4d. System applies data cleaning
  • 4e. Resume from step 5

AF-004-B: Analysis Failure

• At step 9: If analysis code execution fails
  • 9a. System identifies error and provides explanation
  • 9b. System suggests alternative approaches
  • 9c. User can accept suggestions or modify analysis
  • 9d. Resume from step 9

AF-004-C: Insufficient Data

• At step 4: If dataset is too small for analysis
  • 4a. System identifies data insufficiency
  • 4b. System suggests data augmentation or alternative approaches
  • 4c. User can proceed with alternative approach or cancel
  • 4d. Resume from step 5 or end use case

AF-004-D: Model Training Timeout

• At step 12: If model training exceeds time limit
  • 12a. System stops training and provides partial results
  • 12b. System suggests simplified model or parameter adjustment
  • 12c. User can accept suggestions or modify parameters
  • 12d. Resume from step 12

Postconditions:

• Data analysis is completed
• Visualizations and insights are generated
• Analysis notebook is saved
• Results are documented
• Findings are available for presentation

Business Rules:

• BR-020: Data analysis must handle missing values appropriately
• BR-021: Analysis must include statistical significance testing
• BR-022: Visualizations must be publication-ready
• BR-023: Analysis code must be reproducible
• BR-024: Analysis results must be version controlled
• BR-025: Data privacy must be maintained during analysis

Traceability:

• Relates to: FR-008, FR-009, NFR-007, NFR-008

UC-005: Manage Version Control

UC-ID: UC-005
Name: Manage Version Control
Primary Actor: Software Developer, DevOps Engineer
Secondary Actors: Git Repository Service, NLP Service, CI/CD Pipeline Service

Preconditions:

• User is authenticated
• Git repository is initialized
• User has appropriate Git permissions
• Local repository is synchronized

Main Success Scenario:

1. User initiates version control operation with voice command
2. User specifies operation (commit, push, pull, branch, merge) through voice
3. NLP Service extracts version control parameters
4. System validates operation feasibility
5. For commit operation:
   • 5a. System stages specified files
   • 5b. System generates commit message based on changes
   • 5c. User reviews and approves commit message
   • 5d. System commits changes to local repository
6. For push operation:
   • 6a. System verifies local changes are committed
   • 6b. System pushes changes to remote repository
   • 6c. System triggers CI/CD pipeline if configured
7. For pull operation:
   • 7a. System fetches changes from remote repository
   • 7b. System merges changes into local branch
   • 7c. System resolves conflicts if necessary
8. For branch operation:
   • 8a. System creates or switches to specified branch
   • 8b. System updates local workspace
9. For merge operation:
   • 9a. System merges specified branch into current branch
   • 9b. System handles merge conflicts
10. System notifies user of operation completion
11. System logs version control operation

Alternative Flows:

AF-005-A: Merge Conflict

• At step 7c or 9b: If merge conflicts occur
  • a. System identifies conflicting files
  • b. System displays conflict details to user
  • c. User specifies conflict resolution strategy
  • d. System applies resolution
  • e. Resume from step 10

AF-005-B: Permission Denied

• At step 4: If user lacks required permissions
  • 4a. System denies operation
  • 4b. System explains permission requirements
  • 4c. End use case

AF-005-C: Repository Sync Required

• At step 6b: If remote repository has newer changes
  • 6b-i. System requests pull before push
  • 6b-ii. User initiates pull operation
  • 6b-iii. Resume from step 7

AF-005-D: Branch Protection Violation

• At step 6b: If branch is protected
  • 6b-i. System requires pull request
  • 6b-ii. System creates pull request
  • 6b-iii. System notifies reviewers
  • 6b-iv. End use case (awaiting review)

Postconditions:

• Version control operation is completed
• Repository is updated
• CI/CD pipeline is triggered if applicable
• Team is notified of changes
• Operation is logged for audit

Business Rules:

• BR-026: All commits must include descriptive messages
• BR-027: Commits must reference issue tracking system
• BR-028: Protected branches require pull request review
• BR-029: Merge conflicts must be resolved before completion
• BR-030: Version control operations must be logged
• BR-031: Sensitive data must not be committed

Traceability:

• Relates to: FR-010, FR-011, NFR-009, NFR-010

UC-006: Debug Application

UC-ID: UC-006
Name: Debug Application
Primary Actor: Software Developer
Secondary Actors: IDE Integration Service, NLP Service, Monitoring Service

Preconditions:

• User is authenticated as Software Developer
• Application is running or debuggable
• IDE debugger is available
• User has debug permissions
• Error or issue is identified

Main Success Scenario:

1. User initiates debugging session with voice command
2. User describes issue or error through voice
3. NLP Service extracts issue details (error message, location, symptoms)
4. System retrieves relevant logs and stack traces
5. System analyzes error and suggests potential causes
6. System displays error analysis to user
7. User requests specific debugging action (set breakpoint, inspect variable, step through code)
8. System executes debugging action
9. System displays debugging results
10. User continues debugging with additional commands
11. User identifies root cause
12. User requests code fix suggestion
13. System generates fix based on root cause analysis
14. User reviews and approves fix
15. System applies fix to code
16. System re-runs application to verify fix
17. System confirms issue resolution

Alternative Flows:

AF-006-A: Root Cause Not Found

• At step 11: If root cause cannot be identified
  • 11a. System suggests additional debugging steps
  • 11b. System suggests consulting documentation or team
  • 11c. User can continue debugging or escalate
  • 11d. Resume from step 7 or end use case

AF-006-B: Breakpoint Not Set

• At step 8: If breakpoint cannot be set
  • 8a. System explains why breakpoint cannot be set
  • 8b. System suggests alternative debugging approach
  • 8c. User can accept suggestion or try different location
  • 8d. Resume from step 8

AF-006-C: Variable Inspection Fails

• At step 8: If variable cannot be inspected
  • 8a. System explains variable scope or availability issue
  • 8b. System suggests alternative inspection methods
  • 8c. User can accept suggestion or continue
  • 8d. Resume from step 8

AF-006-D: Fix Verification Fails

• At step 16: If fix does not resolve issue
  • 16a. System indicates issue persists
  • 16b. System suggests additional debugging
  • 16c. User can continue debugging or revert fix
  • 16d. Resume from step 7 or revert and end use case

Postconditions:

• Issue is identified and documented
• Root cause is determined
• Fix is applied and verified
• Application is functioning correctly
• Debugging session is logged

Business Rules:

• BR-032: Debugging must not affect production systems
• BR-033: Breakpoints must be cleared after debugging
• BR-034: Debug sessions must be time-limited
• BR-035: Sensitive data must not be exposed during debugging
• BR-036: Debugging operations must be logged
• BR-037: Fixes must be tested before deployment

Traceability:

• Relates to: FR-012, FR-013, NFR-011, NFR-012

UC-007: Deploy Application

UC-ID: UC-007
Name: Deploy Application
Primary Actor: DevOps Engineer, Software Developer
Secondary Actors: CI/CD Pipeline Service, Monitoring Service, NLP Service

Preconditions:

• User is authenticated as DevOps Engineer or authorized Developer
• Application is built and tested
• Deployment configuration is defined
• Target environment is available
• User has deployment permissions

Main Success Scenario:

1. User initiates deployment with voice command
2. User specifies target environment (dev, staging, production) through voice
3. NLP Service extracts deployment parameters
4. System validates deployment prerequisites
5. System retrieves deployment configuration
6. System displays deployment plan to user
7. User reviews and approves deployment plan
8. System triggers CI/CD pipeline
9. System executes build stage
10. System executes test stage
11. System executes deployment stage
12. System performs health checks on deployed application
13. System monitors application performance
14. System confirms successful deployment
15. System notifies team of deployment completion
16. System logs deployment operation

Alternative Flows:

AF-007-A: Build Failure

• At step 9: If build fails
  • 9a. System identifies build error
  • 9b. System displays error details to user
  • 9c. User can fix error and retry or rollback
  • 9d. Resume from step 9 or rollback

AF-007-B: Test Failure

• At step 10: If tests fail
  • 10a. System identifies failing tests
  • 10b. System displays test results to user
  • 10c. User can fix code and retry or rollback
  • 10d. Resume from step 10 or rollback

AF-007-C: Health Check Failure

• At step 12: If health checks fail
  • 12a. System identifies health check failures
  • 12b. System initiates automatic rollback
  • 12c. System notifies user of rollback
  • 12d. End use case

AF-007-D: Deployment Approval Required

• At step 7: If production deployment requires approval
  • 7a. System requests approval from authorized personnel
  • 7b. Authorized personnel reviews and approves/rejects
  • 7c. If approved: Resume from step 8; If rejected: End use case

AF-007-E: Insufficient Resources

• At step 4: If target environment lacks resources
  • 4a. System identifies resource constraints
  • 4b. System suggests alternative environment or scaling
  • 4c. User can accept suggestion or cancel
  • 4d. Resume from step 4 or end use case

Postconditions:

• Application is deployed to target environment
• Application is verified and operational
• Team is notified of deployment
• Deployment is logged and documented
• Monitoring is active on deployed application

Business Rules:

• BR-038: Deployments must follow change management process
• BR-039: Production deployments require approval
• BR-040: Deployment must include automated health checks
• BR-041: Rollback capability must be available
• BR-042: Deployment must be logged with full audit trail
• BR-043: Deployment must not exceed maintenance window

Traceability:

• Relates to: FR-014, FR-015, NFR-013, NFR-014

UC-008: Monitor System Performance

UC-ID: UC-008
Name: Monitor System Performance
Primary Actor: DevOps Engineer, Technical Architect
Secondary Actors: Monitoring Service, NLP Service, Alert Service

Preconditions:

• User is authenticated as DevOps Engineer or Technical Architect
• Monitoring system is operational
• Metrics collection is active
• User has monitoring permissions

Main Success Scenario:

1. User initiates monitoring query with voice command
2. User specifies metrics to monitor (CPU, memory, latency, throughput) through voice
3. NLP Service extracts monitoring parameters (time range, thresholds, services)
4. System retrieves metrics from monitoring service
5. System analyzes metrics and identifies trends
6. System displays metrics and analysis to user
7. User requests specific performance analysis
8. System performs analysis (bottleneck identification, capacity planning)
9. System displays analysis results
10. User requests alert configuration
11. System configures alerts based on user specifications
12. System monitors for alert conditions
13. When alert condition is met:
    • 13a. System generates alert
    • 13b. System notifies user through voice and visual alerts
    • 13c. System provides recommended actions
14. User acknowledges alert
15. System logs monitoring activity

Alternative Flows:

AF-008-A: Metrics Unavailable

• At step 4: If metrics are unavailable
  • 4a. System identifies missing metrics
  • 4b. System suggests alternative metrics or time range
  • 4c. User can accept suggestion or cancel
  • 4d. Resume from step 4 or end use case

AF-008-B: Performance Anomaly Detected

• At step 5: If anomaly is detected
  • 5a. System identifies anomaly and severity
  • 5b. System suggests investigation steps
  • 5c. User can investigate or dismiss
  • 5d. Resume from step 6 or end use case

AF-008-C: Alert Configuration Conflict

• At step 11: If alert configuration conflicts with existing alerts
  • 11a. System identifies conflicts
  • 11b. System suggests resolution
  • 11c. User can accept suggestion or modify configuration
  • 11d. Resume from step 11

Postconditions:

• Performance metrics are analyzed
• Alerts are configured
• User is informed of system status
• Monitoring data is logged
• Recommendations are provided

Business Rules:

• BR-044: Metrics must be collected with <1 minute granularity
• BR-045: Alerts must be triggered within 30 seconds of threshold breach
• BR-046: Alert notifications must be delivered within 10 seconds
• BR-047: Monitoring data must be retained for 90 days
• BR-048: Performance analysis must include trend analysis
• BR-049: Alerts must include recommended actions

Traceability:

• Relates to: FR-016, FR-017, NFR-015, NFR-016

UC-009: Manage Project Tasks

UC-ID: UC-009
Name: Manage Project Tasks
Primary Actor: Project Manager, Software Developer
Secondary Actors: Project Management Service, NLP Service, Notification Service

Preconditions:

• User is authenticated
• Project is selected
• Project management system is accessible
• User has task management permissions

Main Success Scenario:

1. User initiates task management with voice command
2. User specifies task operation (create, update, assign, complete) through voice
3. NLP Service extracts task details (title, description, assignee, priority, deadline)
4. For create operation:
   • 4a. System creates new task with specified details
   • 4b. System assigns task to specified team member
   • 4c. System sets task priority and deadline
5. For update operation:
   • 5a. System retrieves specified task
   • 5b. System updates task with new details
6. For assign operation:
   • 6a. System retrieves task
   • 6b. System assigns task to specified team member
   • 6c. System notifies assignee
7. For complete operation:
   • 7a. System retrieves task
   • 7b. System marks task as complete
   • 7c. System records completion time
8. System updates project status
9. System notifies affected team members
10. System logs task operation

Alternative Flows:

AF-009-A: Task Not Found

• At step 4a, 5a, 6a, or 7a: If task cannot be found
  • a. System notifies user of missing task
  • b. User can create new task or search for different task
  • c. Resume from step 2 or end use case

AF-009-B: Assignee Unavailable

• At step 4b or 6b: If assignee is unavailable
  • b. System suggests alternative assignees
  • c. User can accept suggestion or specify different assignee
  • d. Resume from step 4b or 6b

AF-009-C: Deadline Conflict

• At step 4c: If deadline conflicts with other tasks
  • c. System identifies conflict
  • d. System suggests alternative deadline
  • e. User can accept suggestion or confirm deadline
  • f. Resume from step 4c

Postconditions:

• Task is created, updated, or completed
• Team members are notified
• Project status is updated
• Task operation is logged
• Project timeline is updated

Business Rules:

• BR-050: Tasks must have clear descriptions
• BR-051: Tasks must be assigned to team members
• BR-052: Task deadlines must be realistic
• BR-053: Task priority must be defined
• BR-054: Task completion must be verified
• BR-055: Task changes must be logged

Traceability:

• Relates to: FR-018, FR-019, NFR-017, NFR-018

UC-010: Generate Project Report

UC-ID: UC-010
Name: Generate Project Report
Primary Actor: Project Manager
Secondary Actors: Project Management Service, NLP Service, Data Storage Service

Preconditions:

• User is authenticated as Project Manager
• Project data is available
• Report templates are configured
• User has reporting permissions

Main Success Scenario:

1. User initiates report generation with voice command
2. User specifies report type (status, progress, resource, risk) through voice
3. NLP Service extracts report parameters (time period, scope, format)
4. System retrieves project data
5. System analyzes project metrics
6. System generates report based on template
7. System includes visualizations and charts
8. System displays report preview to user
9. User reviews report and requests modifications
10. System updates report based on feedback
11. User approves final report
12. System exports report in specified format (PDF, Excel, HTML)
13. System stores report in repository
14. System distributes report to stakeholders
15. System logs report generation

Alternative Flows:

AF-010-A: Insufficient Data

• At step 4: If project data is incomplete
  • 4a. System identifies missing data
  • 4b. System suggests data collection or alternative report
  • 4c. User can proceed with available data or cancel
  • 4d. Resume from step 5 or end use case

AF-010-B: Report Generation Failure

• At step 6: If report generation fails
  • 6a. System identifies error
  • 6b. System suggests alternative report type or parameters
  • 6c. User can accept suggestion or cancel
  • 6d. Resume from step 6 or end use case

AF-010-C: Export Format Not Supported

• At step 12: If export format is not supported
  • 12a. System suggests supported formats
  • 12b. User can select alternative format
  • 12c. Resume from step 12

Postconditions:

• Report is generated and formatted
• Report is stored in repository
• Report is distributed to stakeholders
• Report generation is logged
• Report is available for future reference

Business Rules:

• BR-056: Reports must include executive summary
• BR-057: Reports must include key metrics and KPIs
• BR-058: Reports must be generated within 5 minutes
• BR-059: Reports must be exportable in multiple formats
• BR-060: Report data must be current and accurate
• BR-061: Reports must be archived for compliance

Traceability:

• Relates to: FR-020, FR-021, NFR-019, NFR-020

UC-011: Collaborate on Code Review

UC-ID: UC-011
Name: Collaborate on Code Review
Primary Actor: Software Developer, Technical Architect
Secondary Actors: Git Repository Service, NLP Service, Notification Service

Preconditions:

• User is authenticated
• Pull request or code review is initiated
• Code is available for review
• Reviewers are assigned
• User has review permissions

Main Success Scenario:

1. User initiates code review with voice command
2. User specifies pull request or code to review through voice
3. NLP Service extracts review parameters
4. System retrieves code and review context
5. System displays code to user
6. User provides code review comments through voice
7. NLP Service extracts review feedback (issues, suggestions, approvals)
8. System records review comments with line references
9. User requests code quality analysis
10. System performs static analysis and security scanning
11. System displays analysis results
12. User provides additional feedback based on analysis
13. System aggregates all review feedback
14. System notifies code author of review feedback
15. Author can respond to feedback through voice
16. System records author responses
17. If all issues are resolved:
    • 17a. Reviewer approves pull request
    • 17b. System marks pull request as approved
18. System logs code review activity

Alternative Flows:

AF-011-A: Code Quality Issues Found

• At step 11: If significant quality issues are found
  • 11a. System highlights critical issues
  • 11b. System requests changes before approval
  • 11c. Author must address issues
  • 11d. Resume from step 9

AF-011-B: Security Vulnerabilities Detected

• At step 10: If security vulnerabilities are detected
  • 10a. System flags vulnerabilities as critical
  • 10b. System blocks approval until vulnerabilities are fixed
  • 10c. Author must fix vulnerabilities
  • 10d. Resume from step 9

AF-011-C: Reviewer Disagreement

• At step 17: If multiple reviewers disagree
  • 17a. System identifies disagreement
  • 17b. System facilitates discussion between reviewers
  • 17c. Reviewers reach consensus
  • 17d. Resume from step 17

Postconditions:

• Code review is completed
• Feedback is recorded
• Pull request is approved or rejected
• Code author is notified
• Review activity is logged

Business Rules:

• BR-062: Code reviews must be completed within 24 hours
• BR-063: All pull requests must be reviewed before merge
• BR-064: Security issues must block approval
• BR-065: Review feedback must be constructive
• BR-066: Code review comments must be documented
• BR-067: Approved code must meet quality standards

Traceability:

• Relates to: FR-022, FR-023, NFR-021, NFR-022

UC-012: Configure Security Policies

UC-ID: UC-012
Name: Configure Security Policies
Primary Actor: Security Officer, Technical Architect
Secondary Actors: Authentication Service, NLP Service, Data Storage Service

Preconditions:

• User is authenticated as Security Officer or Technical Architect
• Security policy framework is available
• User has security configuration permissions
• System is in maintenance mode or change window

Main Success Scenario:

1. User initiates security policy configuration with voice command
2. User specifies policy type (access control, encryption, audit, compliance) through voice
3. NLP Service extracts policy parameters
4. System retrieves current policy configuration
5. System displays current policy to user
6. User specifies new policy requirements through voice
7. System validates policy against security standards
8. System displays policy validation results
9. User reviews and approves policy
10. System applies new policy
11. System notifies affected users of policy changes
12. System logs policy change with audit trail
13. System monitors policy compliance

Alternative Flows:

AF-012-A: Policy Validation Failure

• At step 7: If policy violates security standards
  • 7a. System identifies violations
  • 7b. System suggests compliant alternatives
  • 7c. User can accept suggestions or modify policy
  • 7d. Resume from step 7

AF-012-B: Policy Conflict

• At step 7: If policy conflicts with existing policies
  • 7a. System identifies conflicts
  • 7b. System suggests resolution
  • 7c. User can accept suggestion or modify policy
  • 7d. Resume from step 7

AF-012-C: Compliance Impact

• At step 9: If policy change affects compliance
  • 9a. System identifies compliance implications
  • 9b. System suggests mitigation measures
  • 9c. User can accept suggestions or modify policy
  • 9d. Resume from step 9

Postconditions:

• Security policy is configured
• Policy is applied to system
• Users are notified of changes
• Policy compliance is monitored
• Policy change is logged

Business Rules:

• BR-068: Security policies must comply with regulations
• BR-069: Policy changes must be approved by Security Officer
• BR-070: Policy changes must be logged with full audit trail
• BR-071: Users must be notified of policy changes
• BR-072: Policy compliance must be monitored continuously
• BR-073: Policy violations must trigger alerts

Traceability:

• Relates to: FR-024, FR-025, NFR-023, NFR-024

UC-013: Handle Voice Command Error

UC-ID: UC-013
Name: Handle Voice Command Error
Primary Actor: All Actors
Secondary Actors: Voice Processing Engine, NLP Service, Error Handling Service

Preconditions:

• Voice command processing has encountered an error
• Error handling system is operational
• User is still in active session

Main Success Scenario:

1. Voice command processing encounters error
2. Error Handling Service captures error details
3. System categorizes error (recognition, parsing, execution, system)
4. System generates appropriate error message
5. System provides error explanation to user through voice
6. System suggests corrective actions
7. User can retry command with modifications
8. System logs error for analysis
9. If error is critical:
   • 9a. System escalates to support team
   • 9b. System provides support contact information
10. System continues session or ends gracefully

Alternative Flows:

AF-013-A: Repeated Error

• At step 7: If same error occurs multiple times
  • 7a. System identifies repeated error
  • 7b. System suggests alternative approaches
  • 7c. System offers to escalate to support
  • 7d. User can accept suggestion or escalate

AF-013-B: System Error

• At step 2: If system error occurs
  • 2a. System attempts recovery
  • 2b. If recovery fails: System gracefully terminates session
  • 2c. System provides error report to user
  • 2d. End use case

Postconditions:

• Error is handled gracefully
• User is informed of error
• Error is logged for analysis
• Session continues or ends appropriately
• Support is engaged if necessary

Business Rules:

• BR-074: All errors must be logged with context
• BR-075: Error messages must be user-friendly
• BR-076: Critical errors must trigger escalation
• BR-077: Error recovery must be attempted automatically
• BR-078: Error patterns must be analyzed for improvement
• BR-079: Users must be provided with support options

Traceability:

• Relates to: FR-026, NFR-025, NFR-026

UC-014: Provide Context-Aware Assistance

UC-ID: UC-014
Name: Provide Context-Aware Assistance
Primary Actor: All Actors
Secondary Actors: NLP Service, Knowledge Base Service, AI Assistant

Preconditions:

• User is in active session
• System has access to user context
• Knowledge base is available
• AI Assistant is operational

Main Success Scenario:

1. User requests assistance through voice command
2. System analyzes user context (current task, project, history)
3. System retrieves relevant information from knowledge base
4. System generates contextual assistance
5. System provides assistance through voice and visual display
6. User can request additional details or examples
7. System provides additional information based on request
8. User can apply suggested solutions or continue independently
9. System logs assistance interaction
10. System updates user context based on interaction

Alternative Flows:

AF-014-A: Insufficient Context

• At step 2: If context is insufficient
  • 2a. System requests clarification from user
  • 2b. User provides additional context
  • 2c. Resume from step 3

AF-014-B: Knowledge Not Found

• At step 3: If relevant knowledge is not found
  • 3a. System searches alternative sources
  • 3b. If found: Resume from step 4
  • 3c. If not found: System suggests related topics or escalates

AF-014-C: Assistance Not Applicable

• At step 4: If generated assistance is not applicable
  • 4a. System identifies mismatch
  • 4b. System requests clarification
  • 4c. Resume from step 2

Postconditions:

• User receives contextual assistance
• Assistance is logged
• User context is updated
• Knowledge base is enhanced if applicable

Business Rules:

• BR-080: Assistance must be contextually relevant
• BR-081: Assistance must be accurate and up-to-date
• BR-082: Assistance must include examples when applicable
• BR-083: Assistance must be personalized to user role
• BR-084: Assistance interactions must be logged
• BR-085: Assistance quality must be continuously improved

Traceability:

• Relates to: FR-027, FR-028, NFR-027, NFR-028

UC-015: Audit and Compliance Reporting

UC-ID: UC-015
Name: Audit and Compliance Reporting
Primary Actor: Security Officer, Compliance Officer
Secondary Actors: Audit Service, Data Storage Service, NLP Service

Preconditions:

• User is authenticated as Security Officer or Compliance Officer
• Audit system is operational
• Compliance requirements are defined
• User has audit permissions

Main Success Scenario:

1. User initiates audit report generation with voice command
2. User specifies audit scope (system, user, operation, time period) through voice
3. NLP Service extracts audit parameters
4. System retrieves audit logs from specified scope
5. System analyzes logs for compliance violations
6. System generates compliance report
7. System displays report to user
8. User reviews report and requests additional analysis
9. System performs additional analysis
10. System displays results
11. User requests remediation recommendations
12. System generates remediation plan
13. System displays remediation plan
14. User approves remediation plan
15. System stores audit report and remediation plan
16. System notifies relevant stakeholders
17. System logs audit activity

Alternative Flows:

AF-015-A: Compliance Violation Found

• At step 5: If compliance violations are found
  • 5a. System flags violations as critical
  • 5b. System generates immediate alert
  • 5c. System notifies Security Officer
  • 5d. Resume from step 6

AF-015-B: Insufficient Audit Data

• At step 4: If audit data is incomplete
  • 4a. System identifies missing data
  • 4b. System suggests alternative scope or time period
  • 4c. User can accept suggestion or proceed with available data
  • 4d. Resume from step 5

AF-015-C: Remediation Not Possible

• At step 12: If remediation is not possible
  • 12a. System identifies constraints
  • 12b. System suggests alternative approaches
  • 12c. User can accept suggestion or escalate
  • 12d. Resume from step 12 or escalate

Postconditions:

• Audit report is generated
• Compliance status is determined
• Remediation plan is created
• Report is stored for compliance
• Stakeholders are notified
• Audit activity is logged

Business Rules:

• BR-086: Audit logs must be immutable
• BR-087: Audit reports must be generated within 24 hours
• BR-088: Compliance violations must be reported immediately
• BR-089: Audit reports must include remediation recommendations
• BR-090: Audit reports must be retained for regulatory period
• BR-091: Audit access must be restricted to authorized personnel

Traceability:

• Relates to: FR-029, FR-030, NFR-029, NFR-030

3. USE CASE DIAGRAM

graph LR
    subgraph Actors
        DEV["👨‍💻 Software Developer"]
        DS["📊 Data Scientist"]
        PM["📋 Project Manager"]
        DEVOPS["⚙️ DevOps Engineer"]
        ARCH["🏗️ Technical Architect"]
        SEC["🔒 Security Officer"]
    end
    
    subgraph System["Voice-Centric Development Agent"]
        UC001["UC-001: Process Voice Command"]
        UC002["UC-002: Design Architecture"]
        UC003["UC-003: Generate Code"]
        UC004["UC-004: Analyze Data"]
        UC005["UC-005: Manage Version Control"]
        UC006["UC-006: Debug Application"]
        UC007["UC-007: Deploy Application"]
        UC008["UC-008: Monitor Performance"]
        UC009["UC-009: Manage Tasks"]
        UC010["UC-010: Generate Report"]
        UC011["UC-011: Code Review"]
        UC012["UC-012: Security Policies"]
        UC013["UC-013: Handle Error"]
        UC014["UC-014: Context Assistance"]
        UC015["UC-015: Audit Reporting"]
    end
    
    subgraph ExternalSystems["External Systems"]
        VOICE["🎤 Voice Engine"]
        NLP["🧠 NLP Service"]
        IDE["💻 IDE Integration"]
        GIT["📦 Git Repository"]
        JUPYTER["📓 Jupyter Notebook"]
        CICD["🔄 CI/CD Pipeline"]
        MONITOR["📈 Monitoring"]
        AUTH["🔐 Authentication"]
        PM_SVC["📊 Project Mgmt"]
        STORAGE["💾 Storage"]
    end
    
    %% Developer Use Cases
    DEV --> UC001
    DEV --> UC003
    DEV --> UC005
    DEV --> UC006
    DEV --> UC011
    DEV --> UC009
    
    %% Data Scientist Use Cases
    DS --> UC001
    DS --> UC004
    DS --> UC005
    DS --> UC009
    
    %% Project Manager Use Cases
    PM --> UC001
    PM --> UC009
    PM --> UC010
    PM --> UC008
    
    %% DevOps Engineer Use Cases
    DEVOPS --> UC001
    DEVOPS --> UC005
    DEVOPS --> UC007
    DEVOPS --> UC008
    DEVOPS --> UC012
    
    %% Technical Architect Use Cases
    ARCH --> UC001
    ARCH --> UC002
    ARCH --> UC008
    ARCH --> UC012
    ARCH --> UC011
    
    %% Security Officer Use Cases
    SEC --> UC001
    SEC --> UC012
    SEC --> UC015
    SEC --> UC008
    
    %% All actors use common use cases
    DEV --> UC013
    DEV --> UC014
    DS --> UC013
    DS --> UC014
    PM --> UC013
    PM --> UC014
    DEVOPS --> UC013
    DEVOPS --> UC014
    ARCH --> UC013
    ARCH --> UC014
    SEC --> UC013
    SEC --> UC014
    
    %% System to External Systems
    UC001 --> VOICE
    UC001 --> NLP
    UC001 --> AUTH
    
    UC002 --> IDE
    UC002 --> STORAGE
    
    UC003 --> IDE
    UC003 --> GIT
    UC003 --> STORAGE
    
    UC004 --> JUPYTER
    UC004 --> STORAGE
    
    UC005 --> GIT
    UC005 --> CICD
    
    UC006 --> IDE
    UC006 --> MONITOR
    
    UC007 --> CICD
    UC007 --> MONITOR
    
    UC008 --> MONITOR
    
    UC009 --> PM_SVC
    UC009 --> STORAGE
    
    UC010 --> PM_SVC
    UC010 --> STORAGE
    
    UC011 --> GIT
    UC011 --> STORAGE
    
    UC012 --> AUTH
    UC012 --> STORAGE
    
    UC015 --> STORAGE
    UC015 --> AUTH
    
    style UC001 fill:#e1f5ff
    style UC002 fill:#f3e5f5
    style UC003 fill:#e8f5e9
    style UC004 fill:#fff3e0
    style UC005 fill:#fce4ec
    style UC006 fill:#f1f8e9
    style UC007 fill:#ede7f6
    style UC008 fill:#e0f2f1
    style UC009 fill:#fff9c4
    style UC010 fill:#f0f4c3
    style UC011 fill:#dcedc8
    style UC012 fill:#ffccbc
    style UC013 fill:#ffebee
    style UC014 fill:#e1bee7
    style UC015 fill:#c8e6c9

4. ACTOR-USE CASE MATRIX

Legend:

• ✓ = Actor participates in use case
• ✗ = Actor does not participate in use case

5. USE CASE INTERACTION SEQUENCES

UC-001: Process Voice Command - Sequence Diagram

sequenceDiagram
    participant User as User
    participant VoiceEngine as Voice Engine
    participant NLPService as NLP Service
    participant Handler as Command Handler
    participant System as System
    participant AuthService as Auth Service
    
    User->>VoiceEngine: Activate voice (wake word/button)
    VoiceEngine->>VoiceEngine: Capture audio stream
    VoiceEngine->>VoiceEngine: Convert speech to text (STT)
    VoiceEngine->>NLPService: Send transcribed text
    
    NLPService->>NLPService: Parse command
    NLPService->>NLPService: Extract intent & entities
    NLPService->>NLPService: Validate syntax
    
    NLPService->>AuthService: Verify authorization
    AuthService->>AuthService: Check permissions
    AuthService-->>NLPService: Authorization result
    
    alt Authorization Success
        NLPService->>Handler: Route to handler
        Handler->>System: Execute command
        System-->>Handler: Command result
        Handler-->>NLPService: Response
        NLPService->>VoiceEngine: Generate response text
        VoiceEngine->>VoiceEngine: Convert text to speech (TTS)
        VoiceEngine-->>User: Play audio response
    else Authorization Failed
        AuthService-->>NLPService: Denied
        NLPService->>VoiceEngine: Generate error message
        VoiceEngine->>VoiceEngine: Convert to speech
        VoiceEngine-->>User: Play error message
    end
    
    System->>System: Log command execution

UC-003: Generate Code - Sequence Diagram

sequenceDiagram
    participant Dev as Developer
    participant NLP as NLP Service
    participant CodeGen as Code Generator
    participant IDE as IDE Service
    participant Git as Git Service
    participant QA as Quality Analyzer
    
    Dev->>NLP: Specify code requirements
    NLP->>NLP: Extract specifications
    NLP->>CodeGen: Request code generation
    
    CodeGen->>CodeGen: Retrieve templates
    CodeGen->>CodeGen: Generate code
    CodeGen->>QA: Analyze code quality
    
    QA->>QA: Check complexity
    QA->>QA: Check security
    QA->>QA: Check performance
    QA-->>CodeGen: Quality report
    
    alt Quality Issues Found
        CodeGen->>CodeGen: Refine code
        CodeGen->>QA: Re-analyze
        QA-->>CodeGen: Updated report
    end
    
    CodeGen-->>Dev: Display generated code
    Dev->>Dev: Review code
    Dev->>CodeGen: Approve code
    
    CodeGen->>IDE: Insert code
    IDE->>IDE: Format code
    IDE->>IDE: Apply standards
    IDE-->>Dev: Code inserted
    
    CodeGen->>Git: Commit code
    Git->>Git: Create commit
    Git-->>CodeGen: Commit successful
    
    CodeGen->>Dev: Notify completion

UC-007: Deploy Application - Sequence Diagram

sequenceDiagram
    participant DevOps as DevOps Engineer
    participant CICD as CI/CD Pipeline
    participant Build as Build Service
    participant Test as Test Service
    participant Deploy as Deploy Service
    participant Monitor as Monitoring
    participant Notify as Notification
    
    DevOps->>CICD: Initiate deployment
    CICD->>CICD: Validate prerequisites
    CICD->>Build: Start build
    
    Build->>Build: Compile code
    Build->>Build: Package application
    Build-->>CICD: Build complete
    
    alt Build Failed
        CICD-->>DevOps: Build failed
        DevOps->>DevOps: Fix issues
    else Build Success
        CICD->>Test: Start tests
        Test->>Test: Run unit tests
        Test->>Test: Run integration tests
        Test-->>CICD: Tests complete
        
        alt Tests Failed
            CICD-->>DevOps: Tests failed
        else Tests Success
            CICD->>Deploy: Start deployment
            Deploy->>Deploy: Deploy to environment
            Deploy->>Monitor: Health check
            
            alt Health Check Failed
                Monitor-->>Deploy: Health check failed
                Deploy->>Deploy: Rollback
                Deploy-->>CICD: Deployment failed
            else Health Check Success
                Monitor-->>Deploy: Health check passed
                Deploy-->>CICD: Deployment successful
                CICD->>Notify: Send notification
                Notify-->>DevOps: Deployment complete
            end
        end
    end

UC-004: Analyze Data - Sequence Diagram

sequenceDiagram
    participant DS as Data Scientist
    participant NLP as NLP Service
    participant Jupyter as Jupyter Service
    participant DataMgmt as Data Management
    participant Analysis as Analysis Engine
    participant Viz as Visualization
    
    DS->>NLP: Specify analysis requirements
    NLP->>NLP: Extract analysis parameters
    NLP->>Jupyter: Load dataset
    
    Jupyter->>DataMgmt: Retrieve dataset
    DataMgmt-->>Jupyter: Dataset loaded
    
    Jupyter->>Analysis: Generate EDA code
    Analysis->>Analysis: Execute EDA
    Analysis->>Viz: Generate visualizations
    Viz-->>Analysis: Visualizations created
    Analysis-->>Jupyter: EDA complete
    
    Jupyter-->>DS: Display results
    DS->>NLP: Request additional analysis
    NLP->>Analysis: Generate analysis code
    
    Analysis->>Analysis: Execute analysis
    Analysis->>Analysis: Calculate statistics
    Analysis-->>Jupyter: Analysis complete
    
    Jupyter-->>DS: Display results
    DS->>NLP: Request model building
    NLP->>Analysis: Generate model code
    
    Analysis->>Analysis: Build model
    Analysis->>Analysis: Train model
    Analysis->>Analysis: Evaluate model
    Analysis-->>Jupyter: Model complete
    
    Jupyter-->>DS: Display model results
    DS->>Jupyter: Export results
    Jupyter->>DataMgmt: Store notebook
    DataMgmt-->>Jupyter: Stored
    Jupyter-->>DS: Export complete

6. USE CASE DEPENDENCIES AND RELATIONSHIPS

graph TD
    UC001["UC-001: Process Voice Command"]
    UC002["UC-002: Design Architecture"]
    UC003["UC-003: Generate Code"]
    UC004["UC-004: Analyze Data"]
    UC005["UC-005: Manage Version Control"]
    UC006["UC-006: Debug Application"]
    UC007["UC-007: Deploy Application"]
    UC008["UC-008: Monitor Performance"]
    UC009["UC-009: Manage Tasks"]
    UC010["UC-010: Generate Report"]
    UC011["UC-011: Code Review"]
    UC012["UC-012: Configure Security"]
    UC013["UC-013: Handle Error"]
    UC014["UC-014: Context Assistance"]
    UC015["UC-015: Audit Reporting"]
    
    %% UC-001 is foundation for all
    UC001 -->|includes| UC002
    UC001 -->|includes| UC003
    UC001 -->|includes| UC004
    UC001 -->|includes| UC005
    UC001 -->|includes| UC006
    UC001 -->|includes| UC007
    UC001 -->|includes| UC008
    UC001 -->|includes| UC009
    UC001 -->|includes| UC010
    UC001 -->|includes| UC011
    UC001 -->|includes| UC012
    UC001 -->|includes| UC015
    
    %% Error handling
    UC001 -->|extends| UC013
    UC002 -->|extends| UC013
    UC003 -->|extends| UC013
    UC004 -->|extends| UC013
    UC005 -->|extends| UC013
    UC006 -->|extends| UC013
    UC007 -->|extends| UC013
    UC008 -->|extends| UC013
    UC009 -->|extends| UC013
    UC010 -->|extends| UC013
    UC011 -->|extends| UC013
    UC012 -->|extends| UC013
    UC015 -->|extends| UC013
    
    %% Context assistance
    UC001 -->|extends| UC014
    UC002 -->|extends| UC014
    UC003 -->|extends| UC014
    UC004 -->|extends| UC014
    UC005 -->|extends| UC014
    UC006 -->|extends| UC014
    UC007 -->|extends| UC014
    UC008 -->|extends| UC014
    UC009 -->|extends| UC014
    UC010 -->|extends| UC014
    UC011 -->|extends| UC014
    UC012 -->|extends| UC014
    UC015 -->|extends| UC014
    
    %% Development workflow
    UC002 -->|precedes| UC003
    UC003 -->|precedes| UC011
    UC011 -->|precedes| UC005
    UC005 -->|precedes| UC007
    UC007 -->|precedes| UC008
    
    %% Code quality
    UC003 -->|related| UC006
    UC006 -->|related| UC011
    
    %% Project management
    UC009 -->|related| UC010
    UC010 -->|related| UC008
    
    %% Security
    UC012 -->|related| UC015
    UC015 -->|related| UC011
    
    style UC001 fill:#e1f5ff,stroke:#01579b,stroke-width:3px
    style UC013 fill:#ffebee,stroke:#b71c1c,stroke-width:2px
    style UC014 fill:#e1bee7,stroke:#4a148c,stroke-width:2px

7. DETAILED BUSINESS RULES REFERENCE

Development-Related Business Rules

Architecture-Related Business Rules

Data Science-Related Business Rules

Version Control-Related Business Rules

Deployment-Related Business Rules

Monitoring-Related Business Rules

Project Management-Related Business Rules

Code Review-Related Business Rules

Security-Related Business Rules

Error Handling-Related Business Rules

Assistance-Related Business Rules

Audit-Related Business Rules

8. ACCEPTANCE CRITERIA SUMMARY

UC-001: Process Voice Command

AC-001-1: System processes voice command within 500ms latency

• Testable: Measure end-to-end latency from voice activation to response
• Acceptance: ≥95% of commands processed within 500ms

AC-001-2: Voice recognition accuracy is ≥95%

• Testable: Compare recognized text with actual spoken text
• Acceptance: Accuracy score ≥95% across diverse accents and environments

AC-001-3: System handles concurrent voice sessions

• Testable: Simulate multiple simultaneous voice sessions
• Acceptance: System maintains performance with ≥10 concurrent sessions

AC-001-4: All commands are logged for audit

• Testable: Verify audit log entries for each command
• Acceptance: 100% of commands logged with timestamp, user, and result

UC-002: Design Software Architecture

AC-002-1: Architecture diagram is generated automatically

• Testable: Request architecture design and verify diagram generation
• Acceptance: Diagram generated within 30 seconds

AC-002-2: Architecture follows organizational standards

• Testable: Validate architecture against standards checklist
• Acceptance: 100% compliance with applicable standards

AC-002-3: Architecture documentation is auto-generated

• Testable: Request documentation and verify completeness
• Acceptance: Documentation includes all required sections

UC-003: Generate Code

AC-003-1: Generated code passes linting checks

• Testable: Run linter on generated code
• Acceptance: Zero linting errors

AC-003-2: Generated code follows project standards

• Testable: Code review against standards
• Acceptance: 100% compliance with coding standards

AC-003-3: Code generation supports multiple languages

• Testable: Generate code in Python, Java, JavaScript, Go
• Acceptance: All specified languages supported

UC-004: Analyze Data

AC-004-1: Data quality issues are identified

• Testable: Analyze dataset with known quality issues
• Acceptance: All issues identified and reported

AC-004-2: Analysis results are reproducible

• Testable: Re-run analysis and compare results
• Acceptance: Results are identical across runs

AC-004-3: Visualizations are publication-ready

• Testable: Review visualization quality and formatting
• Acceptance: Visualizations meet publication standards

UC-005: Manage Version Control

AC-005-1: Merge conflicts are resolved correctly

• Testable: Create merge conflict and verify resolution
• Acceptance: Conflicts resolved without data loss

AC-005-2: Protected branches require review

• Testable: Attempt push to protected branch
• Acceptance: Push blocked until review approved

AC-005-3: All operations are logged

• Testable: Verify Git log entries
• Acceptance: 100% of operations logged

UC-006: Debug Application

AC-006-1: Breakpoints are set correctly

• Testable: Set breakpoint and verify execution stops
• Acceptance: Breakpoint functions as expected

AC-006-2: Variables can be inspected

• Testable: Inspect variable values during debugging
• Acceptance: Variable values displayed correctly

AC-006-3: Root cause is identified

• Testable: Complete debugging session and verify root cause
• Acceptance: Root cause documented and verified

UC-007: Deploy Application

AC-007-1: Deployment completes within SLA

• Testable: Measure deployment time
• Acceptance: Deployment completes within defined SLA

AC-007-2: Health checks pass after deployment

• Testable: Verify health check results
• Acceptance: All health checks pass

AC-007-3: Rollback is available if needed

• Testable: Trigger rollback and verify success
• Acceptance: Rollback completes successfully

UC-008: Monitor Performance

AC-008-1: Metrics are collected with <1 minute granularity

• Testable: Verify metric collection frequency
• Acceptance: Metrics collected every <1 minute

AC-008-2: Alerts are triggered within 30 seconds

• Testable: Trigger alert condition and measure response time
• Acceptance: Alert triggered within 30 seconds

AC-008-3: Alert notifications are delivered within 10 seconds

• Testable: Measure notification delivery time
• Acceptance: Notifications delivered within 10 seconds

UC-009: Manage Tasks

AC-009-1: Tasks are created with all required fields

• Testable: Create task and verify all fields populated
• Acceptance: All required fields present

AC-009-2: Task assignments are tracked

• Testable: Verify task assignment in system
• Acceptance: Assignments tracked and visible

AC-009-3: Task completion is verified

• Testable: Mark task complete and verify status
• Acceptance: Completion status updated correctly

UC-010: Generate Report

AC-010-1: Reports are generated within 5 minutes

• Testable: Measure report generation time
• Acceptance: Report generated within 5 minutes

AC-010-2: Reports include all required sections

• Testable: Verify report completeness
• Acceptance: All required sections present

AC-010-3: Reports are exportable in multiple formats

• Testable: Export report in PDF, Excel, HTML
• Acceptance: All formats export successfully

UC-011: Code Review

AC-011-1: Code reviews are completed within 24 hours

• Testable: Track review completion time
• Acceptance: 95% of reviews completed within 24 hours

AC-011-2: Security issues block approval

• Testable: Attempt to approve code with security issues
• Acceptance: Approval blocked until issues resolved

AC-011-3: Review comments are documented

• Testable: Verify review comments in system
• Acceptance: All comments documented and visible

UC-012: Configure Security

AC-012-1: Policies comply with regulations

• Testable: Validate policies against regulatory requirements
• Acceptance: 100% compliance with regulations

AC-012-2: Policy changes are logged

• Testable: Verify audit log for policy changes
• Acceptance: All changes logged with full details

AC-012-3: Users are notified of changes

• Testable: Verify notification delivery
• Acceptance: All affected users notified

UC-013: Handle Error

AC-013-1: Errors are logged with context

• Testable: Verify error log entries
• Acceptance: All errors logged with full context

AC-013-2: Error messages are user-friendly

• Testable: Review error messages for clarity
• Acceptance: Messages are clear and actionable

AC-013-3: Critical errors trigger escalation

• Testable: Trigger critical error and verify escalation
• Acceptance: Escalation triggered automatically

UC-014: Context Assistance

AC-014-1: Assistance is contextually relevant

• Testable: Request assistance in various contexts
• Acceptance: Assistance matches context ≥90% of time

AC-014-2: Assistance includes examples

• Testable: Request assistance and verify examples provided
• Acceptance: Examples provided when applicable

AC-014-3: Assistance is personalized to role

• Testable: Request assistance as different roles
• Acceptance: Assistance varies by role appropriately

UC-015: Audit Reporting

AC-015-1: Audit reports are generated within 24 hours

• Testable: Request audit report and measure generation time
• Acceptance: Report generated within 24 hours

AC-015-2: Compliance violations are reported immediately

• Testable: Trigger compliance violation and verify reporting
• Acceptance: Violation reported within 5 minutes

AC-015-3: Audit logs are immutable

• Testable: Attempt to modify audit log
• Acceptance: Modification blocked

9. TRACEABILITY MATRIX

graph LR
    subgraph Requirements
        FR1["FR-001: Voice Processing"]
        FR2["FR-002: NLP Processing"]
        FR3["FR-003: Authentication"]
        FR4["FR-004: Architecture Design"]
        FR5["FR-005: Code Generation"]
        FR6["FR-006: Data Analysis"]
        FR7["FR-007: Version Control"]
        FR8["FR-008: Debugging"]
        FR9["FR-009: Deployment"]
        FR10["FR-010: Monitoring"]
        FR11["FR-011: Project Management"]
        FR12["FR-012: Code Review"]
        FR13["FR-013: Security"]
        FR14["FR-014: Audit"]
    end
    
    subgraph UseCases
        UC1["UC-001"]
        UC2["UC-002"]
        UC3["UC-003"]
        UC4["UC-004"]
        UC5["UC-005"]
        UC6["UC-006"]
        UC7["UC-007"]
        UC8["UC-008"]
        UC9["UC-009"]
        UC10["UC-010"]
        UC11["UC-011"]
        UC12["UC-012"]
        UC13["UC-013"]
        UC14["UC-014"]
        UC15["UC-015"]
    end
    
    subgraph BusinessRules
        BR1["BR-001 to BR-007"]
        BR2["BR-008 to BR-013"]
        BR3["BR-014 to BR-019"]
        BR4["BR-020 to BR-025"]
        BR5["BR-026 to BR-031"]
        BR6["BR-038 to BR-043"]
        BR7["BR-044 to BR-049"]
        BR8["BR-050 to BR-061"]
        BR9["BR-062 to BR-067"]
        BR10["BR-068 to BR-073"]
        BR11["BR-074 to BR-079"]
        BR12["BR-080 to BR-085"]
        BR13["BR-086 to BR-091"]
    end
    
    FR1 --> UC1
    FR2 --> UC1
    FR3 --> UC1
    FR4 --> UC2
    FR5 --> UC3
    FR6 --> UC4
    FR7 --> UC5
    FR8 --> UC6
    FR9 --> UC7
    FR10 --> UC8
    FR11 --> UC9
    FR11 --> UC10
    FR12 --> UC11
    FR13 --> UC12
    FR14 --> UC15
    
    UC1 --> BR1
    UC2 --> BR2
    UC3 --> BR3
    UC4 --> BR4
    UC5 --> BR5
    UC7 --> BR6
    UC8 --> BR7
    UC9 --> BR8
    UC10 --> BR8
    UC11 --> BR9
    UC12 --> BR10
    UC13 --> BR11
    UC14 --> BR12
    UC15 --> BR13

10. SUMMARY AND KEY INSIGHTS

Use Case Coverage

The Voice-Centric Software Development and Data Science Agent system includes 15 comprehensive use cases covering:

1. Core Infrastructure (UC-001): Voice command processing foundation
2. Development Activities (UC-002, UC-003, UC-005, UC-006, UC-011): Architecture design, code generation, version control, debugging, code review
3. Data Science Activities (UC-004): Data analysis and exploration
4. Operations (UC-007, UC-008): Deployment and monitoring
5. Project Management (UC-009, UC-010): Task management and reporting
6. Security & Compliance (UC-012, UC-015): Security policies and audit reporting
7. Cross-Cutting Concerns (UC-013, UC-014): Error handling and context-aware assistance

Actor Engagement

• Software Developer: 6 primary use cases + 2 cross-cutting
• Data Scientist: 4 primary use cases + 2 cross-cutting
• Project Manager: 4 primary use cases + 2 cross-cutting
• DevOps Engineer: 5 primary use cases + 2 cross-cutting
• Technical Architect: 5 primary use cases + 2 cross-cutting
• Security Officer: 3 primary use cases + 2 cross-cutting

Business Rules Alignment

• 91 business rules defined across all use cases
• Critical priority: 18 rules (security, compliance, performance)
• High priority: 52 rules (functionality, reliability)
• Medium priority: 21 rules (optimization, user experience)

Key Quality Attributes

• Performance: <500ms voice processing latency, <1 minute metric granularity
• Reliability: 99.9% uptime, automatic error recovery
• Security: Authentication, authorization, audit logging, compliance
• Usability: Context-aware assistance, user-friendly error messages
• Scalability: Support for concurrent users and operations

11. NEXT STEPS

1. Functional Requirements Specification: Develop detailed FR document with system capabilities
2. Non-Functional Requirements: Define performance, security, scalability requirements
3. System Architecture Design: Create detailed architecture diagrams and component specifications
4. Data Model Design: Define data structures and database schema
5. API Specifications: Document all system interfaces and integrations
6. Test Strategy: Develop comprehensive test plans for all use cases
7. Implementation Planning: Create development roadmap and sprint planning

Document Version: 1.0
Last Updated: 2024
Status: Complete
Classification: Technical Documentation

Requirements Specification

Comprehensive Requirements Documentation

Voice-Centric Software Development and Data Science Agent

Table of Contents

1. Functional Requirements
2. Non-Functional Requirements
3. Requirements Traceability Matrix
4. Requirements Dependency Diagram
5. Use Case to Requirements Mapping

FUNCTIONAL REQUIREMENTS

FR-001: Voice Command Recognition and Processing

Description: The system shall capture, process, and interpret voice commands from users in real-time, converting speech to text with high accuracy and supporting multiple languages and accents.

Priority: Must Have

Source: All Stakeholders (Core Functionality)

Acceptance Criteria:

• Voice input is captured with minimum 16kHz sampling rate
• Speech-to-text conversion achieves ≥95% accuracy for standard English accents
• System supports minimum 5 languages (English, Spanish, French, German, Mandarin)
• Processing latency from voice capture to text output ≤200ms (p95)
• System handles background noise levels up to 60dB with ≥90% accuracy
• Supports both continuous and command-based voice input modes
• Voice commands are logged with timestamps for audit trails

Traceability: UC-001 (Voice Command Initiation), UC-002 (Code Generation)

FR-002: Natural Language Understanding and Intent Classification

Description: The system shall parse natural language voice commands to identify user intent, extract parameters, and map commands to appropriate system actions.

Priority: Must Have

Source: Software Developer, Data Scientist, Project Manager

Acceptance Criteria:

• Intent classification accuracy ≥92% for predefined command categories
• System extracts minimum 3 parameter types (action, target, context)
• Ambiguous commands trigger clarification prompts within 500ms
• System maintains context across 5+ consecutive commands
• Supports domain-specific terminology for software development and data science
• Handles negations, conditionals, and complex sentence structures
• Provides confidence scores for intent classification (0-100%)

Traceability: UC-001, UC-003 (Architecture Design), UC-004 (Data Analysis)

FR-003: Code Generation from Voice Commands

Description: The system shall generate syntactically correct, functional code snippets in multiple programming languages based on voice-described requirements.

Priority: Must Have

Source: Software Developer

Acceptance Criteria:

• Generated code compiles/runs without syntax errors ≥90% of the time
• Supports minimum 8 programming languages (Python, Java, C++, JavaScript, Go, Rust, TypeScript, C#)
• Generated code includes inline comments explaining logic
• Code follows language-specific style guides (PEP8, Google Style Guide, etc.)
• System provides code quality metrics (cyclomatic complexity, maintainability index)
• Generated code can be directly inserted into IDE or saved to file
• System suggests optimizations for generated code
• Supports generation of unit tests alongside production code

Traceability: UC-002 (Code Generation), UC-005 (Testing Assistance)

FR-004: Architecture Design and Visualization

Description: The system shall create and visualize software architecture diagrams based on voice-described system designs, supporting multiple architecture patterns.

Priority: Must Have

Source: Technical Architect, Software Developer

Acceptance Criteria:

• System generates architecture diagrams in minimum 3 formats (Mermaid, PlantUML, SVG)
• Supports minimum 5 architecture patterns (Microservices, Monolithic, Event-Driven, Layered, CQRS)
• Diagrams include component relationships, data flows, and communication protocols
• System validates architectural consistency and identifies potential issues
• Diagrams can be exported to documentation tools (Confluence, Notion, GitHub)
• System suggests improvements based on architectural best practices
• Supports real-time diagram updates as requirements change
• Diagrams include deployment topology information

Traceability: UC-003 (Architecture Design), UC-006 (Documentation Generation)

FR-005: Data Analysis and Visualization Request Processing

Description: The system shall interpret voice commands for data analysis tasks and generate appropriate analysis code, visualizations, and insights.

Priority: Must Have

Source: Data Scientist

Acceptance Criteria:

• System generates analysis code for minimum 5 data science libraries (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch)
• Supports minimum 8 visualization types (scatter plots, histograms, heatmaps, time series, box plots, violin plots, 3D plots, interactive dashboards)
• Generated visualizations are publication-ready with proper labels, legends, and formatting
• System performs exploratory data analysis (EDA) automatically when requested
• Supports statistical testing and hypothesis validation
• Generates insights and recommendations from analysis results
• Code includes data validation and error handling
• System suggests appropriate analysis methods based on data characteristics

Traceability: UC-004 (Data Analysis), UC-007 (Model Development)

FR-006: IDE Integration and Code Insertion

Description: The system shall integrate with popular IDEs to enable direct insertion of generated code into active editor windows.

Priority: Must Have

Source: Software Developer

Acceptance Criteria:

• Supports minimum 5 IDEs (VS Code, IntelliJ IDEA, PyCharm, Visual Studio, Sublime Text)
• Code insertion respects current cursor position and indentation
• System maintains IDE syntax highlighting and formatting
• Supports undo/redo operations for inserted code
• Integration works across Windows, macOS, and Linux platforms
• IDE plugin installation is automated and requires <2 minutes
• System detects active IDE and language context automatically
• Supports code insertion into multiple files simultaneously

Traceability: UC-002 (Code Generation), UC-008 (Development Workflow)

FR-007: Git Repository Integration and Version Control

Description: The system shall integrate with Git repositories to enable voice-controlled version control operations and code management.

Priority: Must Have

Source: Software Developer, DevOps Engineer

Acceptance Criteria:

• Supports voice commands for minimum 10 Git operations (commit, push, pull, branch, merge, rebase, tag, stash, cherry-pick, reset)
• System generates meaningful commit messages based on code changes
• Supports multiple Git hosting platforms (GitHub, GitLab, Bitbucket, Azure DevOps)
• Automatic branch creation and switching via voice commands
• Conflict detection and resolution suggestions
• Integration with CI/CD pipelines for automated testing
• Supports code review workflows with voice-based comments
• Maintains audit trail of all Git operations initiated via voice

Traceability: UC-008 (Development Workflow), UC-009 (Collaboration)

FR-008: Project Management and Task Tracking

Description: The system shall enable voice-controlled project management operations including task creation, status updates, and progress tracking.

Priority: Should Have

Source: Project Manager, Software Developer

Acceptance Criteria:

• Supports voice commands for minimum 8 project management operations (create task, update status, assign task, set deadline, add comment, view progress, generate report, schedule meeting)
• Integrates with minimum 3 project management tools (Jira, Asana, Monday.com)
• Automatic task creation from voice commands with extracted parameters
• System generates task descriptions and acceptance criteria
• Supports sprint planning and backlog management via voice
• Real-time progress tracking and burndown chart generation
• Automatic status updates based on code commits and deployments
• Voice-based meeting scheduling with calendar integration

Traceability: UC-010 (Project Management), UC-009 (Collaboration)

FR-009: Real-Time Collaboration and Multi-User Support

Description: The system shall support multiple concurrent users collaborating on the same project with real-time synchronization and conflict resolution.

Priority: Should Have

Source: Project Manager, Software Developer, Data Scientist

Acceptance Criteria:

• Supports minimum 10 concurrent users per project
• Real-time synchronization of changes with <1 second latency
• Automatic conflict detection and resolution for simultaneous edits
• User presence indicators showing who is currently working on what
• Shared voice session support for pair programming
• Activity feed showing all user actions with timestamps
• Permission-based access control for collaborative features
• Automatic backup and recovery of collaborative sessions

Traceability: UC-009 (Collaboration), UC-011 (Pair Programming)

FR-010: Testing and Quality Assurance Assistance

Description: The system shall assist with test case generation, test execution, and quality assurance activities through voice commands.

Priority: Should Have

Source: Software Developer, QA Engineer

Acceptance Criteria:

• Generates unit tests for minimum 5 testing frameworks (JUnit, pytest, Mocha, NUnit, Go testing)
• Supports minimum 4 test types (unit tests, integration tests, end-to-end tests, performance tests)
• Test generation achieves ≥80% code coverage for generated tests
• System suggests edge cases and boundary conditions
• Supports test execution and result reporting via voice
• Integration with CI/CD pipelines for automated test runs
• Generates test reports with pass/fail statistics and performance metrics
• Supports mutation testing and code coverage analysis

Traceability: UC-005 (Testing Assistance), UC-012 (Quality Assurance)

FR-011: Documentation Generation and Management

Description: The system shall automatically generate comprehensive technical documentation from code, architecture, and voice-described requirements.

Priority: Should Have

Source: Technical Architect, Software Developer, Project Manager

Acceptance Criteria:

• Generates documentation in minimum 3 formats (Markdown, HTML, PDF)
• Supports minimum 4 documentation types (API documentation, architecture documentation, user guides, deployment guides)
• Automatic code documentation extraction with docstring generation
• Supports multiple documentation platforms (GitHub Wiki, Confluence, Notion, ReadTheDocs)
• Documentation includes diagrams, code examples, and cross-references
• Version control for documentation with change tracking
• Automatic documentation updates when code changes
• Multi-language documentation support (minimum 5 languages)

Traceability: UC-006 (Documentation Generation), UC-003 (Architecture Design)

FR-012: Machine Learning Model Development Assistance

Description: The system shall assist with machine learning model development including model selection, training, evaluation, and deployment guidance.

Priority: Should Have

Source: Data Scientist

Acceptance Criteria:

• Supports minimum 8 ML frameworks (Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM, Keras, Hugging Face, MLflow)
• Generates code for minimum 10 model types (Linear Regression, Logistic Regression, Decision Trees, Random Forests, SVM, Neural Networks, CNNs, RNNs, Transformers, Ensemble Methods)
• Automatic hyperparameter tuning suggestions
• Model evaluation with minimum 5 metrics (accuracy, precision, recall, F1-score, AUC-ROC)
• Cross-validation and train-test split management
• Feature engineering suggestions and implementation
• Model comparison and selection recommendations
• Deployment guidance for production environments

Traceability: UC-007 (Model Development), UC-013 (ML Pipeline)

FR-013: Database Schema Design and Query Generation

Description: The system shall assist with database design, schema creation, and SQL query generation based on voice-described requirements.

Priority: Should Have

Source: Software Developer, Data Scientist

Acceptance Criteria:

• Supports minimum 5 database systems (PostgreSQL, MySQL, MongoDB, Cassandra, DynamoDB)
• Generates normalized database schemas following minimum 3NF principles
• Automatic primary key, foreign key, and index suggestions
• Generates SQL queries for minimum 8 operation types (SELECT, INSERT, UPDATE, DELETE, JOIN, GROUP BY, aggregations, window functions)
• Query optimization suggestions and execution plan analysis
• Supports both SQL and NoSQL query generation
• Automatic migration script generation for schema changes
• Data validation and constraint generation

Traceability: UC-004 (Data Analysis), UC-014 (Database Design)

FR-014: Deployment and DevOps Automation

Description: The system shall provide voice-controlled deployment and DevOps automation capabilities including infrastructure provisioning and monitoring.

Priority: Should Have

Source: DevOps Engineer, Software Developer

Acceptance Criteria:

• Supports voice commands for minimum 10 deployment operations (deploy, rollback, scale, monitor, alert, provision, configure, backup, restore, update)
• Integrates with minimum 3 cloud platforms (AWS, Azure, Google Cloud)
• Supports minimum 3 container orchestration platforms (Kubernetes, Docker Swarm, ECS)
• Automatic infrastructure-as-code generation (Terraform, CloudFormation, Ansible)
• Deployment pipeline creation and management via voice
• Real-time deployment status monitoring and logging
• Automatic rollback on deployment failures
• Health check and monitoring configuration

Traceability: UC-015 (Deployment), UC-016 (DevOps Automation)

FR-015: Security and Vulnerability Analysis

Description: The system shall analyze code and configurations for security vulnerabilities and provide remediation recommendations.

Priority: Should Have

Source: Security Officer, Software Developer

Acceptance Criteria:

• Detects minimum 15 vulnerability categories (SQL injection, XSS, CSRF, authentication flaws, authorization flaws, insecure deserialization, broken access control, sensitive data exposure, XXE, broken authentication, using components with known vulnerabilities, insufficient logging, missing encryption, hardcoded credentials, insecure dependencies)
• Supports minimum 3 security scanning tools (OWASP ZAP, Snyk, SonarQube)
• Generates remediation code for identified vulnerabilities
• Compliance checking for minimum 3 standards (OWASP Top 10, CWE Top 25, GDPR)
• Automatic dependency vulnerability scanning
• Security report generation with severity levels
• Integration with security incident management systems
• Continuous security monitoring and alerting

Traceability: UC-017 (Security Analysis), UC-018 (Compliance)

FR-016: User Authentication and Authorization

Description: The system shall implement secure user authentication and role-based access control for all features and resources.

Priority: Must Have

Source: Security Officer, All Stakeholders

Acceptance Criteria:

• Supports minimum 3 authentication methods (username/password, OAuth 2.0, SAML 2.0)
• Multi-factor authentication (MFA) support with minimum 2 factors (TOTP, SMS)
• Role-based access control (RBAC) with minimum 5 roles (Admin, Architect, Developer, Data Scientist, Viewer)
• Fine-grained permission management at feature and resource level
• Session management with configurable timeout (default 30 minutes)
• Audit logging of all authentication and authorization events
• Support for single sign-on (SSO) integration
• Password policy enforcement (minimum 12 characters, complexity requirements)

Traceability: UC-019 (User Management), UC-020 (Security)

FR-017: Voice Session Management and Context Preservation

Description: The system shall maintain user context across voice sessions and enable seamless continuation of work.

Priority: Must Have

Source: All Stakeholders

Acceptance Criteria:

• Session state persists across minimum 24 hours of inactivity
• Context includes minimum 10 previous commands and their results
• Users can reference previous commands by number or description
• Automatic context switching between projects/tasks
• Session history is searchable and filterable
• Context can be exported and shared with other users
• Automatic context cleanup after 30 days of inactivity
• Context includes user preferences and customizations

Traceability: UC-001 (Voice Command Initiation), UC-021 (Session Management)

FR-018: Error Handling and Recovery

Description: The system shall provide comprehensive error handling, recovery mechanisms, and user-friendly error messages.

Priority: Must Have

Source: All Stakeholders

Acceptance Criteria:

• All errors are caught and logged with full stack traces
• User-facing error messages are clear and actionable (no technical jargon)
• System suggests recovery actions for common errors
• Automatic retry logic for transient failures (maximum 3 retries with exponential backoff)
• Graceful degradation when optional services are unavailable
• Error recovery options presented via voice interface
• Detailed error logs available for debugging (minimum 30 days retention)
• Error analytics and trending for proactive issue identification

Traceability: UC-022 (Error Handling), All Use Cases

FR-019: Feedback and Continuous Improvement

Description: The system shall collect user feedback and continuously improve based on usage patterns and user satisfaction.

Priority: Could Have

Source: Product Manager, All Stakeholders

Acceptance Criteria:

• Voice-based feedback collection after each major operation
• Feedback includes satisfaction rating (1-5 scale) and optional comments
• System tracks minimum 10 quality metrics (accuracy, latency, usefulness, ease of use, etc.)
• Feedback data is analyzed for trends and improvement opportunities
• A/B testing framework for feature improvements
• User satisfaction dashboard with minimum monthly reporting
• Feedback integration with product roadmap planning
• Automatic alerts for significant quality degradation

Traceability: UC-023 (Feedback), UC-024 (Continuous Improvement)

FR-020: Offline Mode and Caching

Description: The system shall support offline mode with local caching for frequently used features and data.

Priority: Could Have

Source: Software Developer, Data Scientist

Acceptance Criteria:

• Offline mode supports minimum 5 core features (code generation, documentation, architecture design, analysis, testing)
• Local cache stores minimum 100 recent commands and results
• Automatic synchronization when connectivity is restored
• Cache invalidation strategy with configurable TTL (default 24 hours)
• Offline mode clearly indicated to user via voice notification
• Conflict resolution for changes made offline vs. online
• Cache storage limited to 500MB per user
• Secure cache encryption for sensitive data

Traceability: UC-025 (Offline Mode), UC-026 (Caching)

NON-FUNCTIONAL REQUIREMENTS

Performance Requirements

NFR-P-001: Voice Processing Latency

Description: Voice input processing shall complete within specified latency thresholds to ensure responsive user experience.

Priority: Must Have

Acceptance Criteria:

• Speech-to-text conversion: ≤200ms (p95), ≤300ms (p99)
• Intent classification: ≤150ms (p95), ≤250ms (p99)
• Code generation initiation: ≤500ms (p95), ≤1000ms (p99)
• End-to-end voice command processing: ≤1000ms (p95), ≤2000ms (p99)
• Voice output generation: ≤300ms (p95), ≤500ms (p99)
• Measured under normal load conditions (100 concurrent users)

Traceability: FR-001, FR-002, FR-003

NFR-P-002: Code Generation Performance

Description: Code generation operations shall complete within acceptable timeframes based on complexity.

Priority: Must Have

Acceptance Criteria:

• Simple code snippets (<50 lines): ≤2 seconds
• Medium complexity code (50-200 lines): ≤5 seconds
• Complex code (200+ lines): ≤15 seconds
• Architecture diagram generation: ≤3 seconds
• Test case generation: ≤4 seconds
• Documentation generation: ≤10 seconds
• Measured with 100 concurrent users

Traceability: FR-003, FR-010, FR-011

NFR-P-003: Data Analysis Performance

Description: Data analysis operations shall complete within acceptable timeframes based on dataset size.

Priority: Should Have

Acceptance Criteria:

• Analysis of datasets <1GB: ≤5 seconds
• Analysis of datasets 1-10GB: ≤30 seconds
• Analysis of datasets 10-100GB: ≤120 seconds
• Visualization generation: ≤3 seconds
• Statistical testing: ≤5 seconds
• Model training (small datasets): ≤30 seconds
• Measured with 50 concurrent users

Traceability: FR-005, FR-012

NFR-P-004: Throughput and Capacity

Description: System shall handle specified concurrent user load and request throughput.

Priority: Must Have

Acceptance Criteria:

• Support minimum 1000 concurrent voice sessions
• Process minimum 10,000 voice commands per minute
• Support minimum 500 concurrent code generation requests
• Support minimum 100 concurrent data analysis requests
• Database query throughput: minimum 50,000 queries per second
• API throughput: minimum 100,000 requests per second
• Measured under sustained load for minimum 1 hour

Traceability: NFR-S-001, NFR-S-002

NFR-P-005: Memory and Resource Utilization

Description: System components shall operate within specified memory and resource constraints.

Priority: Should Have

Acceptance Criteria:

• Voice processing service: ≤500MB per concurrent user
• Code generation service: ≤1GB per concurrent request
• Data analysis service: ≤2GB per concurrent request
• API gateway: ≤2GB for 1000 concurrent connections
• Database: ≤80% memory utilization under peak load
• CPU utilization: ≤75% under peak load
• Disk I/O: ≤80% utilization under peak load

Traceability: NFR-S-001, NFR-S-002

Scalability Requirements

NFR-S-001: Horizontal Scalability

Description: System architecture shall support horizontal scaling to handle increased load.

Priority: Must Have

Acceptance Criteria:

• Stateless microservices enable linear scaling with added instances
• Load balancing distributes traffic evenly across minimum 10 instances
• Auto-scaling triggers at 70% CPU utilization
• Auto-scaling scales down at 30% CPU utilization
• Scaling operations complete within 2 minutes
• No service interruption during scaling operations
• Database connection pooling supports minimum 10,000 connections
• Cache layer (Redis) supports minimum 100GB data

Traceability: NFR-P-004, NFR-R-001

NFR-S-002: Data Scalability

Description: System shall handle growing data volumes without performance degradation.

Priority: Must Have

Acceptance Criteria:

• Database supports minimum 10TB of data
• Query performance remains constant as data grows (with proper indexing)
• Archive strategy for data older than 1 year
• Data partitioning strategy for tables >1GB
• Backup and recovery time: ≤4 hours for 10TB database
• Data retention policy: minimum 7 years for audit logs
• Time-series data compression: minimum 10:1 ratio
• Search index supports minimum 1 billion documents

Traceability: NFR-P-004, NFR-R-001

NFR-S-003: User Scalability

Description: System shall support growing number of users without performance degradation.

Priority: Must Have

Acceptance Criteria:

• Support minimum 100,000 registered users
• Support minimum 10,000 concurrent active users
• Support minimum 1,000,000 voice commands per day
• Per-user resource allocation remains constant as user base grows
• User onboarding process completes within 5 minutes
• User data isolation and multi-tenancy support
• Tenant-specific resource quotas and limits
• Fair resource allocation across all users

Traceability: NFR-P-004, NFR-R-001

NFR-S-004: Feature Scalability

Description: System architecture shall support addition of new features without major refactoring.

Priority: Should Have

Acceptance Criteria:

• Plugin architecture for new language support
• Extensible intent classification framework
• Modular service architecture for new integrations
• Feature flags for gradual rollout of new features
• A/B testing framework for feature validation
• Backward compatibility for API changes (minimum 2 versions)
• Feature deprecation process with minimum 6-month notice
• Feature documentation and training materials

Traceability: NFR-P-005, NFR-M-001

Security Requirements

NFR-SEC-001: Authentication and Authorization

Description: System shall implement secure authentication and authorization mechanisms.

Priority: Must Have

Acceptance Criteria:

• OAuth 2.0 / OpenID Connect compliance
• SAML 2.0 support for enterprise SSO
• Multi-factor authentication (MFA) mandatory for admin users
• TOTP (Time-based One-Time Password) support
• SMS-based OTP as fallback MFA method
• Session tokens with 30-minute expiration
• Refresh token rotation on each use
• Role-based access control (RBAC) with minimum 5 roles
• Attribute-based access control (ABAC) for fine-grained permissions
• API key authentication for service-to-service communication

Traceability: FR-016, FR-017

NFR-SEC-002: Data Encryption

Description: System shall encrypt sensitive data in transit and at rest.

Priority: Must Have

Acceptance Criteria:

• TLS 1.3 for all data in transit
• Minimum 256-bit AES encryption for data at rest
• Encryption key management with HSM (Hardware Security Module)
• Key rotation every 90 days
• Separate encryption keys for each tenant
• Database-level encryption for sensitive columns
• Encrypted backups with separate key management
• End-to-end encryption for user-to-user communication
• Encryption of voice recordings and transcripts
• Secure key storage with access logging

Traceability: FR-016, FR-015

NFR-SEC-003: Vulnerability Management

Description: System shall implement processes to identify and remediate security vulnerabilities.

Priority: Must Have

Acceptance Criteria:

• Automated dependency scanning (daily)
• Static application security testing (SAST) on every commit
• Dynamic application security testing (DAST) weekly
• Penetration testing quarterly
• Security code review for all changes
• Vulnerability disclosure policy with 90-day remediation SLA
• Security patch deployment within 24 hours for critical vulnerabilities
• Vulnerability tracking and metrics dashboard
• Integration with security incident management
• Security training for all developers (annual)

Traceability: FR-015, FR-016

NFR-SEC-004: Data Privacy and Compliance

Description: System shall comply with data privacy regulations and protect user data.

Priority: Must Have

Acceptance Criteria:

• GDPR compliance (right to be forgotten, data portability, consent management)
• CCPA compliance (data access, deletion, opt-out)
• HIPAA compliance for healthcare data (if applicable)
• SOC 2 Type II certification
• ISO 27001 certification
• Data classification and handling procedures
• Privacy impact assessment (PIA) for new features
• Data retention and deletion policies
• User consent management for data processing
• Privacy policy and terms of service
• Data breach notification within 72 hours

Traceability: FR-016, FR-015

NFR-SEC-005: Audit Logging and Monitoring

Description: System shall maintain comprehensive audit logs and monitor for security threats.

Priority: Must Have

Acceptance Criteria:

• All authentication events logged (login, logout, MFA, password change)
• All authorization events logged (permission changes, role assignments)
• All data access events logged (read, write, delete operations)
• All system configuration changes logged
• Audit logs immutable and tamper-proof
• Audit log retention: minimum 7 years
• Real-time security monitoring and alerting
• Intrusion detection system (IDS) deployment
• Web application firewall (WAF) deployment
• DDoS protection and mitigation
• Security incident response procedures
• Incident response time: <1 hour for critical incidents

Traceability: FR-016, FR-015

NFR-SEC-006: API Security

Description: System APIs shall be secured against common attacks and unauthorized access.

Priority: Must Have

Acceptance Criteria:

• API authentication via OAuth 2.0 or API keys
• Rate limiting: 1000 requests per minute per user
• Request validation and sanitization
• SQL injection prevention (parameterized queries)
• Cross-site scripting (XSS) prevention
• Cross-site request forgery (CSRF) protection
• API versioning with backward compatibility
• API documentation with security guidelines
• API monitoring and anomaly detection
• API gateway with request/response logging
• CORS policy enforcement
• API deprecation process with 6-month notice

Traceability: FR-016, FR-015

Reliability Requirements

NFR-R-001: Availability and Uptime

Description: System shall maintain high availability and uptime.

Priority: Must Have

Acceptance Criteria:

• Target uptime: 99.9% (maximum 43.2 minutes downtime per month)
• Measured across all system components
• Excludes planned maintenance windows (maximum 4 hours per month)
• Redundancy for all critical components (minimum 2 instances)
• Geographic distribution across minimum 3 availability zones
• Automatic failover within 30 seconds
• Health checks every 10 seconds
• Uptime monitoring and alerting
• Uptime SLA with financial penalties for breaches
• Uptime dashboard publicly available

Traceability: NFR-P-001, NFR-R-002

NFR-R-002: Disaster Recovery and Business Continuity

Description: System shall have disaster recovery and business continuity plans.

Priority: Must Have

Acceptance Criteria:

• Recovery Time Objective (RTO): ≤1 hour
• Recovery Point Objective (RPO): ≤15 minutes
• Backup frequency: every 15 minutes for critical data
• Backup retention: minimum 30 days
• Backup testing: monthly
• Disaster recovery drills: quarterly
• Backup geographic distribution (minimum 2 regions)
• Backup encryption and access controls
• Disaster recovery runbook and procedures
• Incident communication plan
• Business continuity plan with minimum 2 recovery sites

Traceability: NFR-R-001, NFR-P-004

NFR-R-003: Error Handling and Recovery

Description: System shall handle errors gracefully and recover automatically.

Priority: Must Have

Acceptance Criteria:

• All exceptions caught and logged
• Automatic retry logic for transient failures (maximum 3 retries)
• Exponential backoff for retries (1s, 2s, 4s)
• Circuit breaker pattern for failing services
• Graceful degradation when optional services unavailable
• User-friendly error messages (no technical jargon)
• Error recovery suggestions provided to users
• Error analytics and trending
• Automatic error notification to support team
• Error resolution time tracking

Traceability: FR-018, NFR-R-001

NFR-R-004: Data Integrity and Consistency

Description: System shall maintain data integrity and consistency.

Priority: Must Have

Acceptance Criteria:

• ACID compliance for database transactions
• Data validation at input and storage
• Referential integrity constraints
• Duplicate detection and prevention
• Data reconciliation processes (daily)
• Checksum verification for data transfers
• Conflict resolution for concurrent updates
• Data consistency monitoring and alerting
• Data repair procedures for corruption
• Data integrity testing (monthly)

Traceability: NFR-R-001, NFR-R-002

NFR-R-005: Monitoring and Alerting

Description: System shall have comprehensive monitoring and alerting capabilities.

Priority: Must Have

Acceptance Criteria:

• Real-time monitoring of all system components
• Minimum 50 key performance indicators (KPIs) tracked
• Alert thresholds defined for all critical metrics
• Alert notification within 1 minute of threshold breach
• Multiple alert channels (email, SMS, Slack, PagerDuty)
• Alert escalation procedures
• Alert fatigue prevention (alert tuning)
• Monitoring dashboard with customizable views
• Historical metrics retention: minimum 1 year
• Monitoring data export for analysis

Traceability: NFR-R-001, NFR-R-002

Usability Requirements

NFR-U-001: Voice Interface Usability

Description: Voice interface shall be intuitive and easy to use for all user types.

Priority: Must Have

Acceptance Criteria:

• Voice commands follow natural language patterns
• System provides voice prompts and guidance
• Voice feedback confirms command receipt and execution
• Error messages are clear and actionable
• Help system accessible via voice (“help”, “what can you do”)
• Command history accessible and searchable
• Voice interface supports minimum 5 languages
• Accent and dialect support for major English variants
• Voice speed adjustment (0.5x to 2x)
• Quiet mode for sensitive environments
• Accessibility features for users with disabilities

Traceability: FR-001, FR-002

NFR-U-002: User Interface Design

Description: User interface shall follow modern design principles and accessibility standards.

Priority: Should Have

Acceptance Criteria:

• WCAG 2.1 Level AA compliance
• Responsive design for minimum 5 screen sizes (mobile, tablet, laptop, desktop, large display)
• Dark mode and light mode support
• Customizable UI themes
• Keyboard navigation support
• Screen reader compatibility
• Color contrast ratio ≥4.5:1 for text
• Font size adjustable (minimum 12pt to 24pt)
• Touch-friendly interface for mobile devices
• Consistent UI patterns across all pages

Traceability: NFR-U-001, NFR-C-001

NFR-U-003: Onboarding and Training

Description: System shall provide effective onboarding and training for new users.

Priority: Should Have

Acceptance Criteria:

• Interactive onboarding tutorial (≤10 minutes)
• Voice-guided setup process
• Sample projects for hands-on learning
• Video tutorials for major features (minimum 5 videos)
• Documentation with examples and use cases
• In-app help and tooltips
• FAQ section with minimum 50 questions
• Community forum for peer support
• Email support with 24-hour response time
• Onboarding completion tracking

Traceability: NFR-U-001, NFR-U-002

NFR-U-004: Personalization and Customization

Description: System shall support user personalization and customization.

Priority: Could Have

Acceptance Criteria:

• User preferences storage (language, timezone, theme, etc.)
• Custom command aliases
• Customizable voice settings (speed, pitch, accent)
• Personalized recommendations based on usage patterns
• Custom dashboard layouts
• Saved searches and filters
• Custom report templates
• Integration preferences
• Notification preferences
• Privacy settings customization

Traceability: NFR-U-001, NFR-U-002

Maintainability Requirements

NFR-M-001: Code Quality and Standards

Description: System code shall maintain high quality and follow established standards.

Priority: Must Have

Acceptance Criteria:

• Code coverage: minimum 80% for unit tests
• Code coverage: minimum 60% for integration tests
• Cyclomatic complexity: maximum 10 per function
• Maintainability index: minimum 70
• Technical debt ratio: maximum 5%
• Code review required for all changes
• Automated code quality checks on every commit
• Coding standards documented and enforced
• Design patterns and best practices followed
• Code duplication: maximum 5%

Traceability: NFR-M-002, NFR-M-003

NFR-M-002: Documentation

Description: System shall have comprehensive documentation.

Priority: Must Have

Acceptance Criteria:

• Architecture documentation with diagrams
• API documentation with examples
• Code documentation with inline comments
• Deployment documentation and runbooks
• Troubleshooting guides
• Configuration documentation
• Security documentation
• Performance tuning guides
• Release notes for each version
• Documentation version control
• Documentation review process
• Documentation update frequency: within 1 week of code changes

Traceability: FR-011, NFR-M-001

NFR-M-003: Version Control and Release Management

Description: System shall use version control and follow release management practices.

Priority: Must Have

Acceptance Criteria:

• Git-based version control with minimum 3 branches (main, develop, feature)
• Semantic versioning (MAJOR.MINOR.PATCH)
• Release notes for each version
• Changelog maintained and updated
• Release process documented and automated
• Release frequency: minimum monthly
• Hotfix process for critical issues
• Version tagging in Git
• Release approval workflow
• Rollback procedures documented

Traceability: FR-007, NFR-M-001

NFR-M-004: Logging and Debugging

Description: System shall provide comprehensive logging and debugging capabilities.

Priority: Should Have

Acceptance Criteria:

• Structured logging with minimum 5 log levels (DEBUG, INFO, WARN, ERROR, FATAL)
• Log retention: minimum 30 days
• Log aggregation and centralization
• Log search and filtering capabilities
• Distributed tracing for request tracking
• Performance profiling tools
• Memory leak detection
• Debug mode for development
• Log sampling for high-volume scenarios
• Log analysis and alerting

Traceability: NFR-M-001, NFR-R-005

NFR-M-005: Testing and Quality Assurance

Description: System shall have comprehensive testing and QA processes.

Priority: Must Have

Acceptance Criteria:

• Unit testing framework (minimum 80% coverage)
• Integration testing framework (minimum 60% coverage)
• End-to-end testing framework
• Performance testing framework
• Security testing framework
• Automated test execution on every commit
• Test result reporting and tracking
• Regression testing for each release
• Load testing before each release
• Accessibility testing for UI changes
• Test data management and cleanup

Traceability: FR-010, NFR-M-001

Compatibility Requirements

NFR-C-001: Browser and Device Compatibility

Description: System shall be compatible with major browsers and devices.

Priority: Must Have

Acceptance Criteria:

• Desktop browsers: Chrome (latest 2 versions), Firefox (latest 2 versions), Safari (latest 2 versions), Edge (latest 2 versions)
• Mobile browsers: Chrome Mobile, Safari Mobile, Firefox Mobile
• Operating systems: Windows 10+, macOS 10.15+, Linux (Ubuntu 20.04+)
• Mobile devices: iOS 13+, Android 10+
• Screen sizes: 320px to 4K (3840x2160)
• Touch and mouse input support
• Keyboard navigation support
• Voice input support on all platforms
• Offline functionality on supported browsers
• Progressive web app (PWA) support

Traceability: NFR-U-002, NFR-C-002

NFR-C-002: IDE and Tool Integration

Description: System shall integrate with popular development tools and IDEs.

Priority: Must Have

Acceptance Criteria:

• IDE support: VS Code, IntelliJ IDEA, PyCharm, Visual Studio, Sublime Text
• Version control: Git, GitHub, GitLab, Bitbucket, Azure DevOps
• Project management: Jira, Asana, Monday.com
• Documentation: Confluence, Notion, GitHub Wiki
• Communication: Slack, Microsoft Teams, Discord
• Cloud platforms: AWS, Azure, Google Cloud
• Container platforms: Docker, Kubernetes
• CI/CD platforms: Jenkins, GitLab CI, GitHub Actions, Azure Pipelines
• Monitoring: Datadog, New Relic, Prometheus, ELK Stack
• Integration testing for each tool

Traceability: FR-006, FR-007, FR-008

NFR-C-003: Programming Language Support

Description: System shall support multiple programming languages.

Priority: Must Have

Acceptance Criteria:

• Supported languages: Python, Java, C++, JavaScript, Go, Rust, TypeScript, C#, PHP, Ruby, Kotlin, Swift
• Language-specific code generation
• Language-specific testing frameworks
• Language-specific documentation generation
• Language-specific linting and formatting
• Language-specific security scanning
• Language-specific performance profiling
• Language-specific debugging support
• New language support process documented

Traceability: FR-003, FR-010

NFR-C-004: Database System Support

Description: System shall support multiple database systems.

Priority: Should Have

Acceptance Criteria:

• Relational databases: PostgreSQL, MySQL, Oracle, SQL Server
• NoSQL databases: MongoDB, Cassandra, DynamoDB, Redis
• Time-series databases: InfluxDB, Prometheus
• Search engines: Elasticsearch, Solr
• Data warehouses: Snowflake, BigQuery, Redshift
• Database-specific query generation
• Database-specific optimization recommendations
• Database-specific backup and recovery
• Database-specific monitoring and alerting
• Database migration support

Traceability: FR-014, NFR-C-002

NFR-C-005: API and Protocol Support

Description: System shall support standard APIs and protocols.

Priority: Should Have

Acceptance Criteria:

• REST API support
• GraphQL support
• gRPC support
• WebSocket support
• MQTT support
• HTTP/2 and HTTP/3 support
• OAuth 2.0 and OpenID Connect
• SAML 2.0 support
• API rate limiting and throttling
• API versioning and deprecation
• API documentation generation
• API testing and monitoring

Traceability: NFR-C-002, NFR-SEC-006

REQUIREMENTS TRACEABILITY MATRIX

graph TD
    UC1["UC-001: Voice Command Initiation"]
    UC2["UC-002: Code Generation"]
    UC3["UC-003: Architecture Design"]
    UC4["UC-004: Data Analysis"]
    UC5["UC-005: Testing Assistance"]
    UC6["UC-006: Documentation Generation"]
    UC7["UC-007: Model Development"]
    UC8["UC-008: Development Workflow"]
    UC9["UC-009: Collaboration"]
    UC10["UC-010: Project Management"]
    UC11["UC-011: Pair Programming"]
    UC12["UC-012: Quality Assurance"]
    UC13["UC-013: ML Pipeline"]
    UC14["UC-014: Database Design"]
    UC15["UC-015: Deployment"]
    UC16["UC-016: DevOps Automation"]
    UC17["UC-017: Security Analysis"]
    UC18["UC-018: Compliance"]
    UC19["UC-019: User Management"]
    UC20["UC-020: Security"]
    UC21["UC-021: Session Management"]
    UC22["UC-022: Error Handling"]
    UC23["UC-023: Feedback"]
    UC24["UC-024: Continuous Improvement"]
    UC25["UC-025: Offline Mode"]
    UC26["UC-026: Caching"]
    
    FR1["FR-001: Voice Recognition"]
    FR2["FR-002: NLU & Intent"]
    FR3["FR-003: Code Generation"]
    FR4["FR-004: Architecture Design"]
    FR5["FR-005: Data Analysis"]
    FR6["FR-006: IDE Integration"]
    FR7["FR-007: Git Integration"]
    FR8["FR-008: Project Management"]
    FR9["FR-009: Collaboration"]
    FR10["FR-010: Testing"]
    FR11["FR-011: Documentation"]
    FR12["FR-012: ML Development"]
    FR13["FR-013: Database Design"]
    FR14["FR-014: Deployment"]
    FR15["FR-015: Security Analysis"]
    FR16["FR-016: Auth & Authz"]
    FR17["FR-017: Session Management"]
    FR18["FR-018: Error Handling"]
    FR19["FR-019: Feedback"]
    FR20["FR-020: Offline Mode"]
    
    NFR_P1["NFR-P-001: Voice Latency"]
    NFR_P2["NFR-P-002: Code Gen Performance"]
    NFR_P3["NFR-P-003: Data Analysis Performance"]
    NFR_P4["NFR-P-004: Throughput"]
    NFR_P5["NFR-P-005: Resource Utilization"]
    NFR_S1["NFR-S-001: Horizontal Scalability"]
    NFR_S2["NFR-S-002: Data Scalability"]
    NFR_S3["NFR-S-003: User Scalability"]
    NFR_S4["NFR-S-004: Feature Scalability"]
    NFR_SEC1["NFR-SEC-001: Auth & Authz"]
    NFR_SEC2["NFR-SEC-002: Encryption"]
    NFR_SEC3["NFR-SEC-003: Vulnerability Mgmt"]
    NFR_SEC4["NFR-SEC-004: Privacy & Compliance"]
    NFR_SEC5["NFR-SEC-005: Audit Logging"]
    NFR_SEC6["NFR-SEC-006: API Security"]
    NFR_R1["NFR-R-001: Availability"]
    NFR_R2["NFR-R-002: Disaster Recovery"]
    NFR_R3["NFR-R-003: Error Handling"]
    NFR_R4["NFR-R-004: Data Integrity"]
    NFR_R5["NFR-R-005: Monitoring"]
    NFR_U1["NFR-U-001: Voice Usability"]
    NFR_U2["NFR-U-002: UI Design"]
    NFR_U3["NFR-U-003: Onboarding"]
    NFR_U4["NFR-U-004: Personalization"]
    NFR_M1["NFR-M-001: Code Quality"]
    NFR_M2["NFR-M-002: Documentation"]
    NFR_M3["NFR-M-003: Version Control"]
    NFR_M4["NFR-M-004: Logging"]
    NFR_M5["NFR-M-005: Testing & QA"]
    NFR_C1["NFR-C-001: Browser Compat"]
    NFR_C2["NFR-C-002: Tool Integration"]
    NFR_C3["NFR-C-003: Language Support"]
    NFR_C4["NFR-C-004: Database Support"]
    NFR_C5["NFR-C-005: API Support"]
    
    UC1 --> FR1
    UC1 --> FR2
    UC1 --> FR17
    UC1 --> FR18
    
    UC2 --> FR3
    UC2 --> FR6
    UC2 --> FR10
    
    UC3 --> FR4
    UC3 --> FR11
    
    UC4 --> FR5
    UC4 --> FR13
    
    UC5 --> FR10
    UC5 --> FR12
    
    UC6 --> FR11
    
    UC7 --> FR12
    UC7 --> FR5
    
    UC8 --> FR6
    UC8 --> FR7
    UC8 --> FR3
    
    UC9 --> FR9
    UC9 --> FR8
    
    UC10 --> FR8
    
    UC11 --> FR9
    UC11 --> FR6
    
    UC12 --> FR10
    UC12 --> FR15
    
    UC13 --> FR12
    UC13 --> FR5
    
    UC14 --> FR13
    
    UC15 --> FR14
    
    UC16 --> FR14
    
    UC17 --> FR15
    
    UC18 --> FR15
    
    UC19 --> FR16
    
    UC20 --> FR16
    UC20 --> FR15
    
    UC21 --> FR17
    
    UC22 --> FR18
    
    UC23 --> FR19
    
    UC24 --> FR19
    
    UC25 --> FR20
    
    UC26 --> FR20
    
    FR1 --> NFR_P1
    FR1 --> NFR_U1
    FR1 --> NFR_C1
    
    FR2 --> NFR_P1
    FR2 --> NFR_P2
    
    FR3 --> NFR_P2
    FR3 --> NFR_C3
    
    FR4 --> NFR_P2
    
    FR5 --> NFR_P3
    FR5 --> NFR_C4
    
    FR6 --> NFR_C2
    
    FR7 --> NFR_C2
    
    FR8 --> NFR_S3
    
    FR9 --> NFR_S1
    FR9 --> NFR_R1
    
    FR10 --> NFR_M5
    
    FR11 --> NFR_M2
    
    FR12 --> NFR_C3
    FR12 --> NFR_C4
    
    FR13 --> NFR_C4
    
    FR14 --> NFR_C2
    
    FR15 --> NFR_SEC3
    FR15 --> NFR_SEC4
    
    FR16 --> NFR_SEC1
    FR16 --> NFR_SEC2
    FR16 --> NFR_SEC5
    
    FR17 --> NFR_R1
    
    FR18 --> NFR_R3
    
    FR19 --> NFR_U4
    
    FR20 --> NFR_P5
    
    NFR_P1 --> NFR_P4
    NFR_P2 --> NFR_P4
    NFR_P3 --> NFR_P4
    
    NFR_P4 --> NFR_S1
    NFR_P5 --> NFR_S1
    
    NFR_S1 --> NFR_S2
    NFR_S1 --> NFR_S3
    
    NFR_SEC1 --> NFR_SEC5
    NFR_SEC2 --> NFR_SEC5
    NFR_SEC3 --> NFR_SEC5
    NFR_SEC4 --> NFR_SEC5
    NFR_SEC6 --> NFR_SEC5
    
    NFR_R1 --> NFR_R2
    NFR_R1 --> NFR_R5
    
    NFR_R3 --> NFR_R4
    
    NFR_U1 --> NFR_U2
    NFR_U1 --> NFR_U3
    
    NFR_M1 --> NFR_M2
    NFR_M1 --> NFR_M3
    NFR_M1 --> NFR_M4
    NFR_M1 --> NFR_M5
    
    NFR_C1 --> NFR_C2
    NFR_C2 --> NFR_C3
    NFR_C2 --> NFR_C4
    NFR_C2 --> NFR_C5
    
    style UC1 fill:#e1f5ff
    style UC2 fill:#e1f5ff
    style UC3 fill:#e1f5ff
    style UC4 fill:#e1f5ff
    style UC5 fill:#e1f5ff
    style UC6 fill:#e1f5ff
    style UC7 fill:#e1f5ff
    style UC8 fill:#e1f5ff
    style UC9 fill:#e1f5ff
    style UC10 fill:#e1f5ff
    style UC11 fill:#e1f5ff
    style UC12 fill:#e1f5ff
    style UC13 fill:#e1f5ff
    style UC14 fill:#e1f5ff
    style UC15 fill:#e1f5ff
    style UC16 fill:#e1f5ff
    style UC17 fill:#e1f5ff
    style UC18 fill:#e1f5ff
    style UC19 fill:#e1f5ff
    style UC20 fill:#e1f5ff
    style UC21 fill:#e1f5ff
    style UC22 fill:#e1f5ff
    style UC23 fill:#e1f5ff
    style UC24 fill:#e1f5ff
    style UC25 fill:#e1f5ff
    style UC26 fill:#e1f5ff
    
    style FR1 fill:#c8e6c9
    style FR2 fill:#c8e6c9
    style FR3 fill:#c8e6c9
    style FR4 fill:#c8e6c9
    style FR5 fill:#c8e6c9
    style FR6 fill:#c8e6c9
    style FR7 fill:#c8e6c9
    style FR8 fill:#c8e6c9
    style FR9 fill:#c8e6c9
    style FR10 fill:#c8e6c9
    style FR11 fill:#c8e6c9
    style FR12 fill:#c8e6c9
    style FR13 fill:#c8e6c9
    style FR14 fill:#c8e6c9
    style FR15 fill:#c8e6c9
    style FR16 fill:#c8e6c9
    style FR17 fill:#c8e6c9
    style FR18 fill:#c8e6c9
    style FR19 fill:#c8e6c9
    style FR20 fill:#c8e6c9
    
    style NFR_P1 fill:#fff9c4
    style NFR_P2 fill:#fff9c4
    style NFR_P3 fill:#fff9c4
    style NFR_P4 fill:#fff9c4
    style NFR_P5 fill:#fff9c4
    style NFR_S1 fill:#fff9c4
    style NFR_S2 fill:#fff9c4
    style NFR_S3 fill:#fff9c4
    style NFR_S4 fill:#fff9c4
    style NFR_SEC1 fill:#ffccbc
    style NFR_SEC2 fill:#ffccbc
    style NFR_SEC3 fill:#ffccbc
    style NFR_SEC4 fill:#ffccbc
    style NFR_SEC5 fill:#ffccbc
    style NFR_SEC6 fill:#ffccbc
    style NFR_R1 fill:#f8bbd0
    style NFR_R2 fill:#f8bbd0
    style NFR_R3 fill:#f8bbd0
    style NFR_R4 fill:#f8bbd0
    style NFR_R5 fill:#f8bbd0
    style NFR_U1 fill:#d1c4e9
    style NFR_U2 fill:#d1c4e9
    style NFR_U3 fill:#d1c4e9
    style NFR_U4 fill:#d1c4e9
    style NFR_M1 fill:#b2dfdb
    style NFR_M2 fill:#b2dfdb
    style NFR_M3 fill:#b2dfdb
    style NFR_M4 fill:#b2dfdb
    style NFR_M5 fill:#b2dfdb
    style NFR_C1 fill:#ffe0b2
    style NFR_C2 fill:#ffe0b2
    style NFR_C3 fill:#ffe0b2
    style NFR_C4 fill:#ffe0b2
    style NFR_C5 fill:#ffe0b2

REQUIREMENTS DEPENDENCY DIAGRAM

Core Functional Requirements Dependencies

graph TD
    FR1["FR-001: Voice Recognition<br/>Priority: Must Have"]
    FR2["FR-002: NLU & Intent<br/>Priority: Must Have"]
    FR3["FR-003: Code Generation<br/>Priority: Must Have"]
    FR4["FR-004: Architecture Design<br/>Priority: Must Have"]
    FR5["FR-005: Data Analysis<br/>Priority: Must Have"]
    FR6["FR-006: IDE Integration<br/>Priority: Must Have"]
    FR7["FR-007: Git Integration<br/>Priority: Must Have"]
    FR8["FR-008: Project Management<br/>Priority: Should Have"]
    FR9["FR-009: Collaboration<br/>Priority: Should Have"]
    FR10["FR-010: Testing<br/>Priority: Should Have"]
    FR11["FR-011: Documentation<br/>Priority: Should Have"]
    FR12["FR-012: ML Development<br/>Priority: Should Have"]
    FR13["FR-013: Database Design<br/>Priority: Should Have"]
    FR14["FR-014: Deployment<br/>Priority: Should Have"]
    FR15["FR-015: Security Analysis<br/>Priority: Should Have"]
    FR16["FR-016: Auth & Authz<br/>Priority: Must Have"]
    FR17["FR-017: Session Management<br/>Priority: Must Have"]
    FR18["FR-018: Error Handling<br/>Priority: Must Have"]
    FR19["FR-019: Feedback<br/>Priority: Could Have"]
    FR20["FR-020: Offline Mode<br/>Priority: Could Have"]
    
    FR1 --> FR2
    FR2 --> FR3
    FR2 --> FR4
    FR2 --> FR5
    FR2 --> FR8
    FR2 --> FR12
    FR2 --> FR13
    FR2 --> FR14
    FR2 --> FR15
    
    FR3 --> FR6
    FR3 --> FR10
    FR3 --> FR11
    
    FR4 --> FR11
    
    FR5 --> FR12
    FR5 --> FR13
    
    FR6 --> FR7
    FR6 --> FR9
    
    FR7 --> FR8
    FR7 --> FR9
    
    FR8 --> FR9
    
    FR9 --> FR11
    
    FR10 --> FR11
    
    FR12 --> FR14
    
    FR13 --> FR14
    
    FR15 --> FR16
    
    FR16 --> FR17
    FR16 --> FR18
    
    FR17 --> FR20
    
    FR18 --> FR19
    
    FR20 --> FR19
    
    style FR1 fill:#c8e6c9,stroke:#2e7d32,stroke-width:3px
    style FR2 fill:#c8e6c9,stroke:#2e7d32,stroke-width:3px
    style FR3 fill:#c8e6c9,stroke:#2e7d32,stroke-width:3px
    style FR4 fill:#c8e6c9,stroke:#2e7d32,stroke-width:3px
    style FR5 fill:#c8e6c9,stroke:#2e7d32,stroke-width:3px
    style FR6 fill:#c8e6c9,stroke:#2e7d32,stroke-width:3px
    style FR7 fill:#c8e6c9,stroke:#2e7d32,stroke-width:3px
    style FR8 fill:#a5d6a7,stroke:#558b2f,stroke-width:2px
    style FR9 fill:#a5d6a7,stroke:#558b2f,stroke-width:2px
    style FR10 fill:#a5d6a7,stroke:#558b2f,stroke-width:2px
    style FR11 fill:#a5d6a7,stroke:#558b2f,stroke-width:2px
    style FR12 fill:#a5d6a7,stroke:#558b2f,stroke-width:2px
    style FR13 fill:#a5d6a7,stroke:#558b2f,stroke-width:2px
    style FR14 fill:#a5d6a7,stroke:#558b2f,stroke-width:2px
    style FR15 fill:#a5d6a7,stroke:#558b2f,stroke-width:2px
    style FR16 fill:#c8e6c9,stroke:#2e7d32,stroke-width:3px
    style FR17 fill:#c8e6c9,stroke:#2e7d32,stroke-width:3px
    style FR18 fill:#c8e6c9,stroke:#2e7d32,stroke-width:3px
    style FR19 fill:#81c784,stroke:#9ccc65,stroke-width:1px
    style FR20 fill:#81c784,stroke:#9ccc65,stroke-width:1px

Non-Functional Requirements Dependencies

graph TD
    NFR_P1["NFR-P-001: Voice Latency<br/>Target: ≤200ms p95"]
    NFR_P2["NFR-P-002: Code Gen Performance<br/>Target: ≤15s for complex"]
    NFR_P3["NFR-P-003: Data Analysis Performance<br/>Target: ≤120s for 100GB"]
    NFR_P4["NFR-P-004: Throughput<br/>Target: 100k req/s"]
    NFR_P5["NFR-P-005: Resource Utilization<br/>Target: ≤75% CPU"]
    
    NFR_S1["NFR-S-001: Horizontal Scalability<br/>Target: Linear scaling"]
    NFR_S2["NFR-S-002: Data Scalability<br/>Target: 10TB database"]
    NFR_S3["NFR-S-003: User Scalability<br/>Target: 100k users"]
    NFR_S4["NFR-S-004: Feature Scalability<br/>Target: Plugin architecture"]
    
    NFR_SEC1["NFR-SEC-001: Auth & Authz<br/>OAuth 2.0, SAML 2.0, MFA"]
    NFR_SEC2["NFR-SEC-002: Encryption<br/>TLS 1.3, AES-256"]
    NFR_SEC3["NFR-SEC-003: Vulnerability Mgmt<br/>SAST, DAST, Pen Testing"]
    NFR_SEC4["NFR-SEC-004: Privacy & Compliance<br/>GDPR, CCPA, SOC 2"]
    NFR_SEC5["NFR-SEC-005: Audit Logging<br/>7-year retention"]
    NFR_SEC6["NFR-SEC-006: API Security<br/>Rate limiting, validation"]
    
    NFR_R1["NFR-R-001: Availability<br/>Target: 99.9% uptime"]
    NFR_R2["NFR-R-002: Disaster Recovery<br/>RTO: ≤1h, RPO: ≤15m"]
    NFR_R3["NFR-R-003: Error Handling<br/>Automatic retry & recovery"]
    NFR_R4["NFR-R-004: Data Integrity<br/>ACID compliance"]
    NFR_R5["NFR-R-005: Monitoring<br/>50+ KPIs tracked"]
    
    NFR_U1["NFR-U-001: Voice Usability<br/>Natural language support"]
    NFR_U2["NFR-U-002: UI Design<br/>WCAG 2.1 Level AA"]
    NFR_U3["NFR-U-003: Onboarding<br/>≤10 min tutorial"]
    NFR_U4["NFR-U-004: Personalization<br/>Custom preferences"]
    
    NFR_M1["NFR-M-001: Code Quality<br/>80% coverage, max 10 complexity"]
    NFR_M2["NFR-M-002: Documentation<br/>Comprehensive & updated"]
    NFR_M3["NFR-M-003: Version Control<br/>Semantic versioning"]
    NFR_M4["NFR-M-004: Logging<br/>30-day retention"]
    NFR_M5["NFR-M-005: Testing & QA<br/>Automated testing"]
    
    NFR_C1["NFR-C-001: Browser Compat<br/>Chrome, Firefox, Safari, Edge"]
    NFR_C2["NFR-C-002: Tool Integration<br/>5+ IDEs, 8+ platforms"]
    NFR_C3["NFR-C-003: Language Support<br/>12+ languages"]
    NFR_C4["NFR-C-004: Database Support<br/>SQL & NoSQL"]
    NFR_C5["NFR-C-005: API Support<br/>REST, GraphQL, gRPC"]
    
    NFR_P1 --> NFR_P4
    NFR_P2 --> NFR_P4
    NFR_P3 --> NFR_P4
    NFR_P5 --> NFR_P4
    
    NFR_P4 --> NFR_S1
    NFR_P4 --> NFR_S2
    NFR_P4 --> NFR_S3
    
    NFR_S1 --> NFR_R1
    NFR_S2 --> NFR_R1
    NFR_S3 --> NFR_R1
    
    NFR_SEC1 --> NFR_SEC5
    NFR_SEC2 --> NFR_SEC5
    NFR_SEC3 --> NFR_SEC5
    NFR_SEC4 --> NFR_SEC5
    NFR_SEC6 --> NFR_SEC5
    
    NFR_SEC5 --> NFR_R1
    
    NFR_R1 --> NFR_R2
    NFR_R1 --> NFR_R5
    
    NFR_R3 --> NFR_R4
    NFR_R4 --> NFR_R1
    
    NFR_U1 --> NFR_U2
    NFR_U2 --> NFR_U3
    NFR_U3 --> NFR_U4
    
    NFR_M1 --> NFR_M2
    NFR_M1 --> NFR_M3
    NFR_M1 --> NFR_M4
    NFR_M1 --> NFR_M5
    
    NFR_M5 --> NFR_R1
    
    NFR_C1 --> NFR_C2
    NFR_C2 --> NFR_C3
    NFR_C2 --> NFR_C4
    NFR_C2 --> NFR_C5
    
    style NFR_P1 fill:#fff9c4,stroke:#f57f17,stroke-width:2px
    style NFR_P2 fill:#fff9c4,stroke:#f57f17,stroke-width:2px
    style NFR_P3 fill:#fff9c4,stroke:#f57f17,stroke-width:2px
    style NFR_P4 fill:#fff9c4,stroke:#f57f17,stroke-width:3px
    style NFR_P5 fill:#fff9c4,stroke:#f57f17,stroke-width:2px
    
    style NFR_S1 fill:#fff9c4,stroke:#f57f17,stroke-width:2px
    style NFR_S2 fill:#fff9c4,stroke:#f57f17,stroke-width:2px
    style NFR_S3 fill:#fff9c4,stroke:#f57f17,stroke-width:2px
    style NFR_S4 fill:#fff9c4,stroke:#f57f17,stroke-width:2px
    
    style NFR_SEC1 fill:#ffccbc,stroke:#d84315,stroke-width:2px
    style NFR_SEC2 fill:#ffccbc,stroke:#d84315,stroke-width:2px
    style NFR_SEC3 fill:#ffccbc,stroke:#d84315,stroke-width:2px
    style NFR_SEC4 fill:#ffccbc,stroke:#d84315,stroke-width:2px
    style NFR_SEC5 fill:#ffccbc,stroke:#d84315,stroke-width:3px
    style NFR_SEC6 fill:#ffccbc,stroke:#d84315,stroke-width:2px
    
    style NFR_R1 fill:#f8bbd0,stroke:#c2185b,stroke-width:3px
    style NFR_R2 fill:#f8bbd0,stroke:#c2185b,stroke-width:2px
    style NFR_R3 fill:#f8bbd0,stroke:#c2185b,stroke-width:2px
    style NFR_R4 fill:#f8bbd0,stroke:#c2185b,stroke-width:2px
    style NFR_R5 fill:#f8bbd0,stroke:#c2185b,stroke-width:2px
    
    style NFR_U1 fill:#d1c4e9,stroke:#512da8,stroke-width:2px
    style NFR_U2 fill:#d1c4e9,stroke:#512da8,stroke-width:2px
    style NFR_U3 fill:#d1c4e9,stroke:#512da8,stroke-width:2px
    style NFR_U4 fill:#d1c4e9,stroke:#512da8,stroke-width:2px
    
    style NFR_M1 fill:#b2dfdb,stroke:#00695c,stroke-width:2px
    style NFR_M2 fill:#b2dfdb,stroke:#00695c,stroke-width:2px
    style NFR_M3 fill:#b2dfdb,stroke:#00695c,stroke-width:2px
    style NFR_M4 fill:#b2dfdb,stroke:#00695c,stroke-width:2px
    style NFR_M5 fill:#b2dfdb,stroke:#00695c,stroke-width:2px
    
    style NFR_C1 fill:#ffe0b2,stroke:#e65100,stroke-width:2px
    style NFR_C2 fill:#ffe0b2,stroke:#e65100,stroke-width:2px
    style NFR_C3 fill:#ffe0b2,stroke:#e65100,stroke-width:2px
    style NFR_C4 fill:#ffe0b2,stroke:#e65100,stroke-width:2px
    style NFR_C5 fill:#ffe0b2,stroke:#e65100,stroke-width:2px

USE CASE TO REQUIREMENTS MAPPING

UC-001: Voice Command Initiation

Description: User initiates system interaction through voice command

Primary Actor: All Stakeholders

Functional Requirements:

• FR-001: Voice Command Recognition and Processing
• FR-002: Natural Language Understanding and Intent Classification
• FR-017: Voice Session Management and Context Preservation
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-P-001: Voice Processing Latency (≤200ms p95)
• NFR-U-001: Voice Interface Usability
• NFR-C-001: Browser and Device Compatibility
• NFR-R-005: Monitoring and Alerting

Test Cases:

• TC-001-001: Verify voice capture at 16kHz sampling rate
• TC-001-002: Verify speech-to-text accuracy ≥95%
• TC-001-003: Verify processing latency ≤200ms p95
• TC-001-004: Verify context preservation across commands
• TC-001-005: Verify error handling for invalid input

UC-002: Code Generation

Description: User requests code generation for specific requirements

Primary Actor: Software Developer

Functional Requirements:

• FR-002: Natural Language Understanding and Intent Classification
• FR-003: Code Generation from Voice Commands
• FR-006: IDE Integration and Code Insertion
• FR-010: Testing and Quality Assurance Assistance
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-P-002: Code Generation Performance (≤15s for complex)
• NFR-C-003: Programming Language Support (12+ languages)
• NFR-M-001: Code Quality and Standards (80% coverage)
• NFR-M-005: Testing and Quality Assurance

Test Cases:

• TC-002-001: Verify code generation for Python function
• TC-002-002: Verify code generation for Java class
• TC-002-003: Verify code insertion into VS Code
• TC-002-004: Verify generated code compiles without errors
• TC-002-005: Verify code follows PEP8 style guide
• TC-002-006: Verify unit test generation
• TC-002-007: Verify code quality metrics calculation

UC-003: Architecture Design

Description: User designs software architecture through voice commands

Primary Actor: Technical Architect, Software Developer

Functional Requirements:

• FR-002: Natural Language Understanding and Intent Classification
• FR-004: Architecture Design and Visualization
• FR-011: Documentation Generation and Management
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-P-002: Code Generation Performance
• NFR-C-002: Tool Integration (Mermaid, PlantUML)
• NFR-M-002: Documentation
• NFR-U-002: UI Design

Test Cases:

• TC-003-001: Verify microservices architecture generation
• TC-003-002: Verify architecture diagram export to Mermaid
• TC-003-003: Verify architecture validation
• TC-003-004: Verify documentation generation
• TC-003-005: Verify architecture pattern suggestions

UC-004: Data Analysis

Description: User performs data analysis through voice commands

Primary Actor: Data Scientist

Functional Requirements:

• FR-002: Natural Language Understanding and Intent Classification
• FR-005: Data Analysis and Visualization Request Processing
• FR-013: Database Schema Design and Query Generation
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-P-003: Data Analysis Performance (≤120s for 100GB)
• NFR-C-004: Database System Support (SQL & NoSQL)
• NFR-M-005: Testing and Quality Assurance
• NFR-R-004: Data Integrity and Consistency

Test Cases:

• TC-004-001: Verify exploratory data analysis generation
• TC-004-002: Verify visualization generation (scatter plot)
• TC-004-003: Verify statistical testing
• TC-004-004: Verify SQL query generation
• TC-004-005: Verify data validation
• TC-004-006: Verify analysis performance for 100GB dataset

UC-005: Testing Assistance

Description: User receives assistance with test case generation and execution

Primary Actor: Software Developer, QA Engineer

Functional Requirements:

• FR-002: Natural Language Understanding and Intent Classification
• FR-010: Testing and Quality Assurance Assistance
• FR-003: Code Generation from Voice Commands
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-M-005: Testing and Quality Assurance (80% coverage)
• NFR-C-003: Programming Language Support
• NFR-P-002: Code Generation Performance
• NFR-R-005: Monitoring and Alerting

Test Cases:

• TC-005-001: Verify unit test generation for Python
• TC-005-002: Verify integration test generation
• TC-005-003: Verify test execution and reporting
• TC-005-004: Verify code coverage calculation
• TC-005-005: Verify edge case suggestions

UC-006: Documentation Generation

Description: User generates technical documentation from code and architecture

Primary Actor: Technical Architect, Software Developer

Functional Requirements:

• FR-002: Natural Language Understanding and Intent Classification
• FR-011: Documentation Generation and Management
• FR-004: Architecture Design and Visualization
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-M-002: Documentation (Markdown, HTML, PDF)
• NFR-P-002: Code Generation Performance
• NFR-C-002: Tool Integration (Confluence, Notion)
• NFR-U-002: UI Design

Test Cases:

• TC-006-001: Verify API documentation generation
• TC-006-002: Verify architecture documentation generation
• TC-006-003: Verify documentation export to Markdown
• TC-006-004: Verify documentation export to PDF
• TC-006-005: Verify documentation version control

UC-007: Model Development

Description: User develops machine learning models with system assistance

Primary Actor: Data Scientist

Functional Requirements:

• FR-002: Natural Language Understanding and Intent Classification
• FR-012: Machine Learning Model Development Assistance
• FR-005: Data Analysis and Visualization Request Processing
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-P-003: Data Analysis Performance
• NFR-C-003: Programming Language Support (Python, R)
• NFR-C-004: Database System Support
• NFR-M-005: Testing and Quality Assurance

Test Cases:

• TC-007-001: Verify model selection recommendations
• TC-007-002: Verify hyperparameter tuning suggestions
• TC-007-003: Verify model evaluation metrics calculation
• TC-007-004: Verify cross-validation implementation
• TC-007-005: Verify feature engineering suggestions

UC-008: Development Workflow

Description: User manages development workflow including code insertion and version control

Primary Actor: Software Developer

Functional Requirements:

• FR-003: Code Generation from Voice Commands
• FR-006: IDE Integration and Code Insertion
• FR-007: Git Repository Integration and Version Control
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-C-002: Tool Integration (5+ IDEs, Git platforms)
• NFR-P-002: Code Generation Performance
• NFR-M-003: Version Control and Release Management
• NFR-R-005: Monitoring and Alerting

Test Cases:

• TC-008-001: Verify code insertion into VS Code
• TC-008-002: Verify Git commit with auto-generated message
• TC-008-003: Verify branch creation and switching
• TC-008-004: Verify merge conflict detection
• TC-008-005: Verify CI/CD pipeline integration

UC-009: Collaboration

Description: Multiple users collaborate on the same project in real-time

Primary Actor: Software Developer, Data Scientist, Project Manager

Functional Requirements:

• FR-009: Real-Time Collaboration and Multi-User Support
• FR-008: Project Management and Task Tracking
• FR-006: IDE Integration and Code Insertion
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-S-001: Horizontal Scalability (10 concurrent users)
• NFR-R-001: Availability (99.9% uptime)
• NFR-P-004: Throughput (100k req/s)
• NFR-SEC-005: Audit Logging

Test Cases:

• TC-009-001: Verify real-time synchronization (<1s latency)
• TC-009-002: Verify conflict detection and resolution
• TC-009-003: Verify user presence indicators
• TC-009-004: Verify activity feed generation
• TC-009-005: Verify permission-based access control

UC-010: Project Management

Description: Project manager tracks project progress and manages tasks

Primary Actor: Project Manager

Functional Requirements:

• FR-008: Project Management and Task Tracking
• FR-002: Natural Language Understanding and Intent Classification
• FR-009: Real-Time Collaboration and Multi-User Support
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-C-002: Tool Integration (Jira, Asana, Monday.com)
• NFR-S-003: User Scalability (100k users)
• NFR-U-002: UI Design
• NFR-R-005: Monitoring and Alerting

Test Cases:

• TC-010-001: Verify task creation from voice command
• TC-010-002: Verify status update via voice
• TC-010-003: Verify sprint planning
• TC-010-004: Verify burndown chart generation
• TC-010-005: Verify Jira integration

UC-011: Pair Programming

Description: Two developers collaborate on code through shared voice session

Primary Actor: Software Developer

Functional Requirements:

• FR-009: Real-Time Collaboration and Multi-User Support
• FR-006: IDE Integration and Code Insertion
• FR-003: Code Generation from Voice Commands
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-R-001: Availability (99.9% uptime)
• NFR-P-001: Voice Processing Latency (≤200ms p95)
• NFR-S-001: Horizontal Scalability
• NFR-SEC-005: Audit Logging

Test Cases:

• TC-011-001: Verify shared voice session creation
• TC-011-002: Verify real-time code synchronization
• TC-011-003: Verify voice communication quality
• TC-011-004: Verify session recording and playback
• TC-011-005: Verify session termination and cleanup

UC-012: Quality Assurance

Description: QA team performs quality assurance and testing activities

Primary Actor: QA Engineer, Software Developer

Functional Requirements:

• FR-010: Testing and Quality Assurance Assistance
• FR-015: Security and Vulnerability Analysis
• FR-002: Natural Language Understanding and Intent Classification
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-M-005: Testing and Quality Assurance (80% coverage)
• NFR-SEC-003: Vulnerability Management
• NFR-P-002: Code Generation Performance
• NFR-R-005: Monitoring and Alerting

Test Cases:

• TC-012-001: Verify security vulnerability detection
• TC-012-002: Verify test case generation
• TC-012-003: Verify test execution and reporting
• TC-012-004: Verify code coverage analysis
• TC-012-005: Verify compliance checking

UC-013: ML Pipeline

Description: Data scientist creates end-to-end machine learning pipeline

Primary Actor: Data Scientist

Functional Requirements:

• FR-012: Machine Learning Model Development Assistance
• FR-005: Data Analysis and Visualization Request Processing
• FR-013: Database Schema Design and Query Generation
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-P-003: Data Analysis Performance
• NFR-C-003: Programming Language Support (Python)
• NFR-C-004: Database System Support
• NFR-M-005: Testing and Quality Assurance

Test Cases:

• TC-013-001: Verify data pipeline generation
• TC-013-002: Verify model training code generation
• TC-013-003: Verify model evaluation code generation
• TC-013-004: Verify deployment code generation
• TC-013-005: Verify pipeline monitoring setup

UC-014: Database Design

Description: Developer designs database schema through voice commands

Primary Actor: Software Developer, Data Scientist

Functional Requirements:

• FR-013: Database Schema Design and Query Generation
• FR-002: Natural Language Understanding and Intent Classification
• FR-004: Architecture Design and Visualization
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-C-004: Database System Support (SQL & NoSQL)
• NFR-R-004: Data Integrity and Consistency
• NFR-P-002: Code Generation Performance
• NFR-M-002: Documentation

Test Cases:

• TC-014-001: Verify schema generation for PostgreSQL
• TC-014-002: Verify schema normalization
• TC-014-003: Verify index suggestions
• TC-014-004: Verify SQL query generation
• TC-014-005: Verify migration script generation

UC-015: Deployment

Description: DevOps engineer deploys application to production

Primary Actor: DevOps Engineer, Software Developer

Functional Requirements:

• FR-014: Deployment and DevOps Automation
• FR-002: Natural Language Understanding and Intent Classification
• FR-007: Git Repository Integration and Version Control
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-C-002: Tool Integration (AWS, Azure, GCP, Kubernetes)
• NFR-R-001: Availability (99.9% uptime)
• NFR-R-002: Disaster Recovery (RTO ≤1h)
• NFR-R-005: Monitoring and Alerting

Test Cases:

• TC-015-001: Verify deployment to AWS
• TC-015-002: Verify deployment to Kubernetes
• TC-015-003: Verify deployment status monitoring
• TC-015-004: Verify automatic rollback on failure
• TC-015-005: Verify health check configuration

UC-016: DevOps Automation

Description: DevOps engineer automates infrastructure and operations

Primary Actor: DevOps Engineer

Functional Requirements:

• FR-014: Deployment and DevOps Automation
• FR-002: Natural Language Understanding and Intent Classification
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-C-002: Tool Integration (Terraform, Ansible, CloudFormation)
• NFR-S-001: Horizontal Scalability
• NFR-R-001: Availability (99.9% uptime)
• NFR-R-005: Monitoring and Alerting

Test Cases:

• TC-016-001: Verify infrastructure provisioning
• TC-016-002: Verify auto-scaling configuration
• TC-016-003: Verify monitoring setup
• TC-016-004: Verify backup configuration
• TC-016-005: Verify disaster recovery setup

UC-017: Security Analysis

Description: Security officer analyzes code and configurations for vulnerabilities

Primary Actor: Security Officer, Software Developer

Functional Requirements:

• FR-015: Security and Vulnerability Analysis
• FR-002: Natural Language Understanding and Intent Classification
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-SEC-003: Vulnerability Management
• NFR-SEC-004: Privacy and Compliance
• NFR-SEC-005: Audit Logging
• NFR-M-002: Documentation

Test Cases:

• TC-017-001: Verify SQL injection detection
• TC-017-002: Verify XSS vulnerability detection
• TC-017-003: Verify hardcoded credential detection
• TC-017-004: Verify dependency vulnerability scanning
• TC-017-005: Verify remediation code generation

UC-018: Compliance

Description: Organization ensures compliance with regulations and standards

Primary Actor: Security Officer, Compliance Officer

Functional Requirements:

• FR-015: Security and Vulnerability Analysis
• FR-016: User Authentication and Authorization
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-SEC-004: Privacy and Compliance (GDPR, CCPA, SOC 2)
• NFR-SEC-005: Audit Logging (7-year retention)
• NFR-M-002: Documentation
• NFR-R-005: Monitoring and Alerting

Test Cases:

• TC-018-001: Verify GDPR compliance
• TC-018-002: Verify CCPA compliance
• TC-018-003: Verify SOC 2 compliance
• TC-018-004: Verify audit log retention
• TC-018-005: Verify compliance reporting

UC-019: User Management

Description: Administrator manages users and permissions

Primary Actor: Admin, Security Officer

Functional Requirements:

• FR-016: User Authentication and Authorization
• FR-002: Natural Language Understanding and Intent Classification
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-SEC-001: Authentication and Authorization
• NFR-SEC-005: Audit Logging
• NFR-U-003: Onboarding and Training
• NFR-R-005: Monitoring and Alerting

Test Cases:

• TC-019-001: Verify user creation
• TC-019-002: Verify role assignment
• TC-019-003: Verify permission management
• TC-019-004: Verify MFA setup
• TC-019-005: Verify user deactivation

UC-020: Security

Description: System maintains security across all operations

Primary Actor: Security Officer, All Stakeholders

Functional Requirements:

• FR-016: User Authentication and Authorization
• FR-015: Security and Vulnerability Analysis
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-SEC-001: Authentication and Authorization
• NFR-SEC-002: Data Encryption (TLS 1.3, AES-256)
• NFR-SEC-005: Audit Logging
• NFR-SEC-006: API Security

Test Cases:

• TC-020-001: Verify TLS 1.3 encryption
• TC-020-002: Verify AES-256 encryption at rest
• TC-020-003: Verify API rate limiting
• TC-020-004: Verify request validation
• TC-020-005: Verify audit log integrity

UC-021: Session Management

Description: System manages user sessions and context

Primary Actor: All Stakeholders

Functional Requirements:

• FR-017: Voice Session Management and Context Preservation
• FR-016: User Authentication and Authorization
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-R-001: Availability (99.9% uptime)
• NFR-SEC-005: Audit Logging
• NFR-P-001: Voice Processing Latency
• NFR-U-001: Voice Interface Usability

Test Cases:

• TC-021-001: Verify session creation
• TC-021-002: Verify context preservation
• TC-021-003: Verify session timeout
• TC-021-004: Verify session recovery
• TC-021-005: Verify session cleanup

UC-022: Error Handling

Description: System handles errors gracefully and provides recovery options

Primary Actor: All Stakeholders

Functional Requirements:

• FR-018: Error Handling and Recovery
• FR-002: Natural Language Understanding and Intent Classification

Non-Functional Requirements:

• NFR-R-003: Error Handling and Recovery
• NFR-R-005: Monitoring and Alerting
• NFR-M-004: Logging and Debugging
• NFR-U-001: Voice Interface Usability

Test Cases:

• TC-022-001: Verify error logging
• TC-022-002: Verify automatic retry logic
• TC-022-003: Verify user-friendly error messages
• TC-022-004: Verify error recovery suggestions
• TC-022-005: Verify error analytics

UC-023: Feedback

Description: System collects user feedback for continuous improvement

Primary Actor: All Stakeholders

Functional Requirements:

• FR-019: Feedback and Continuous Improvement
• FR-002: Natural Language Understanding and Intent Classification

Non-Functional Requirements:

• NFR-U-001: Voice Interface Usability
• NFR-U-004: Personalization and Customization
• NFR-R-005: Monitoring and Alerting
• NFR-M-004: Logging and Debugging

Test Cases:

• TC-023-001: Verify feedback collection
• TC-023-002: Verify satisfaction rating
• TC-023-003: Verify feedback analysis
• TC-023-004: Verify trend identification
• TC-023-005: Verify feedback integration with roadmap

UC-024: Continuous Improvement

Description: System continuously improves based on usage and feedback

Primary Actor: Product Manager, Development Team

Functional Requirements:

• FR-019: Feedback and Continuous Improvement
• FR-002: Natural Language Understanding and Intent Classification

Non-Functional Requirements:

• NFR-U-004: Personalization and Customization
• NFR-M-001: Code Quality and Standards
• NFR-R-005: Monitoring and Alerting
• NFR-S-004: Feature Scalability

Test Cases:

• TC-024-001: Verify A/B testing framework
• TC-024-002: Verify feature flag management
• TC-024-003: Verify quality metrics tracking
• TC-024-004: Verify improvement suggestions
• TC-024-005: Verify rollout automation

UC-025: Offline Mode

Description: User can work offline with local caching

Primary Actor: Software Developer, Data Scientist

Functional Requirements:

• FR-020: Offline Mode and Caching
• FR-001: Voice Command Recognition and Processing
• FR-018: Error Handling and Recovery

Non-Functional Requirements:

• NFR-P-005: Memory and Resource Utilization (500MB cache)
• NFR-R-004: Data Integrity and Consistency
• NFR-U-001: Voice Interface Usability
• NFR-C-001: Browser and Device Compatibility

Test Cases:

• TC-025-001: Verify offline mode activation
• TC-025-002: Verify local cache storage
• TC-025-003: Verify offline command execution
• TC-025-004: Verify synchronization on reconnect
• TC-025-005: Verify conflict resolution

UC-026: Caching

Description: System caches frequently used data and commands

Primary Actor: All Stakeholders

Functional Requirements:

• FR-020: Offline Mode and Caching
• FR-001: Voice Command Recognition and Processing

Non-Functional Requirements:

• NFR-P-005: Memory and Resource Utilization
• NFR-P-004: Throughput and Capacity
• NFR-R-004: Data Integrity and Consistency
• NFR-SEC-002: Data Encryption

Test Cases:

• TC-026-001: Verify cache hit rate
• TC-026-002: Verify cache invalidation
• TC-026-003: Verify cache encryption
• TC-026-004: Verify cache cleanup
• TC-026-005: Verify cache performance improvement

REQUIREMENTS TRACEABILITY MATRIX TABLE

SUMMARY STATISTICS

Functional Requirements Summary

Non-Functional Requirements Summary

Use Cases Summary

REQUIREMENTS VALIDATION CHECKLIST

• All requirements have unique identifiers
• All requirements have clear, testable descriptions
• All requirements have defined priorities
• All requirements have identified sources
• All requirements have acceptance criteria
• All requirements have traceability links
• All requirements are consistent and non-conflicting
• All requirements are feasible within constraints
• All requirements are aligned with project goals
• All requirements have associated test cases
• All requirements follow naming conventions
• All requirements are measurable and verifiable

NEXT STEPS

1. Requirements Review: Conduct stakeholder review and approval
2. Requirements Prioritization: Finalize MoSCoW prioritization with stakeholders
3. Requirements Refinement: Address feedback and clarifications
4. Test Case Development: Develop detailed test cases for each requirement
5. Architecture Design: Begin system architecture design based on requirements
6. Implementation Planning: Create implementation roadmap and sprint planning
7. Continuous Monitoring: Track requirement changes and updates throughout project lifecycle

Document Version: 1.0
Last Updated: 2024
Status: Draft - Awaiting Stakeholder Review
Next Review Date: Upon stakeholder feedback

System Architecture

Voice-Centric Software Development and Data Science Agent

Comprehensive Architecture Documentation

Table of Contents

1. System Context Diagram (C4 Level 1)
2. Container Diagram (C4 Level 2)
3. Component Diagram (C4 Level 3)
4. Deployment Diagram
5. Technology Stack Summary
6. Architecture Decision Records
7. Data Flow Architecture
8. Security Architecture
9. Integration Architecture

System Context Diagram (C4 Level 1)

Overview

The Voice-Centric Agent operates as a central hub connecting developers and data scientists with their development tools, data platforms, and collaboration systems through natural language voice interfaces.

graph TB
    subgraph Users["👥 Users"]
        DEV["Developer"]
        DS["Data Scientist"]
        PM["Project Manager"]
        DEVOPS["DevOps Engineer"]
    end
    
    subgraph VoiceAgent["🎤 Voice-Centric Agent System"]
        AGENT["Voice AI Agent<br/>NLP Engine<br/>Task Orchestration"]
    end
    
    subgraph ExternalSystems["🔗 External Systems"]
        IDE["IDE Integration<br/>VS Code, JetBrains"]
        GIT["Git Repository<br/>GitHub, GitLab"]
        JUPYTER["Jupyter Notebooks<br/>Data Analysis"]
        JIRA["Project Management<br/>Jira, Asana"]
        SLACK["Communication<br/>Slack, Teams"]
        CLOUD["Cloud Platforms<br/>AWS, GCP, Azure"]
        MONITOR["Monitoring<br/>Datadog, Prometheus"]
    end
    
    DEV --> AGENT
    DS --> AGENT
    PM --> AGENT
    DEVOPS --> AGENT
    
    AGENT --> IDE
    AGENT --> GIT
    AGENT --> JUPYTER
    AGENT --> JIRA
    AGENT --> SLACK
    AGENT --> CLOUD
    AGENT --> MONITOR
    
    style AGENT fill:#4A90E2,stroke:#2E5C8A,stroke-width:3px,color:#fff
    style Users fill:#E8F4F8,stroke:#4A90E2,stroke-width:2px
    style ExternalSystems fill:#F0F0F0,stroke:#666,stroke-width:2px

System Responsibilities

• Voice Input Processing: Capture and process voice commands with <500ms latency
• Natural Language Understanding: Parse developer intent and technical context
• Task Orchestration: Route requests to appropriate external systems
• Real-time Feedback: Provide voice and visual feedback on task execution
• Context Management: Maintain conversation history and project context
• Security & Compliance: Enforce authentication, authorization, and data privacy

Container Diagram (C4 Level 2)

High-Level Architecture

graph TB
    subgraph ClientLayer["🖥️ Client Layer"]
        WEB["Web Dashboard<br/>React + TypeScript<br/>Real-time UI"]
        MOBILE["Mobile App<br/>React Native<br/>Voice Interface"]
        VOICE["Voice Client<br/>WebRTC<br/>Audio Streaming"]
    end
    
    subgraph APIGateway["🚪 API Gateway & Load Balancing"]
        LB["Load Balancer<br/>Nginx/HAProxy"]
        APIGW["API Gateway<br/>Kong/AWS API Gateway"]
    end
    
    subgraph CoreServices["⚙️ Core Microservices"]
        VOICE_SVC["Voice Processing Service<br/>Python/FastAPI<br/>Audio Processing"]
        NLP_SVC["NLP Service<br/>Python/FastAPI<br/>Intent Recognition"]
        TASK_SVC["Task Orchestration Service<br/>Node.js/Express<br/>Workflow Management"]
        CONTEXT_SVC["Context Management Service<br/>Node.js/Express<br/>State Management"]
        INTEGRATION_SVC["Integration Service<br/>Python/FastAPI<br/>External APIs"]
    end
    
    subgraph DataLayer["💾 Data Layer"]
        POSTGRES["Primary Database<br/>PostgreSQL<br/>Relational Data"]
        REDIS["Cache Layer<br/>Redis<br/>Session & Cache"]
        MONGO["Document Store<br/>MongoDB<br/>Conversation Logs"]
        VECTOR_DB["Vector Database<br/>Pinecone/Weaviate<br/>Embeddings"]
    end
    
    subgraph ExternalIntegrations["🔌 External Integrations"]
        IDE_ADAPTER["IDE Adapter<br/>VS Code Extension"]
        GIT_ADAPTER["Git Adapter<br/>GitHub/GitLab API"]
        JUPYTER_ADAPTER["Jupyter Adapter<br/>Notebook API"]
        JIRA_ADAPTER["Jira Adapter<br/>REST API"]
        CLOUD_ADAPTER["Cloud Adapter<br/>AWS/GCP/Azure SDK"]
    end
    
    subgraph SupportServices["🛠️ Support Services"]
        AUTH["Authentication Service<br/>OAuth 2.0/JWT"]
        LOGGING["Logging Service<br/>ELK Stack"]
        MONITORING["Monitoring Service<br/>Prometheus/Grafana"]
        QUEUE["Message Queue<br/>RabbitMQ/Kafka"]
    end
    
    %% Client to Gateway
    WEB --> LB
    MOBILE --> LB
    VOICE --> LB
    LB --> APIGW
    
    %% Gateway to Services
    APIGW --> VOICE_SVC
    APIGW --> NLP_SVC
    APIGW --> TASK_SVC
    APIGW --> CONTEXT_SVC
    APIGW --> INTEGRATION_SVC
    
    %% Services to Data Layer
    VOICE_SVC --> REDIS
    NLP_SVC --> VECTOR_DB
    TASK_SVC --> POSTGRES
    CONTEXT_SVC --> MONGO
    CONTEXT_SVC --> REDIS
    INTEGRATION_SVC --> POSTGRES
    
    %% Services to External
    TASK_SVC --> IDE_ADAPTER
    TASK_SVC --> GIT_ADAPTER
    TASK_SVC --> JUPYTER_ADAPTER
    TASK_SVC --> JIRA_ADAPTER
    INTEGRATION_SVC --> CLOUD_ADAPTER
    
    %% Support Services
    VOICE_SVC --> AUTH
    NLP_SVC --> AUTH
    TASK_SVC --> QUEUE
    VOICE_SVC --> LOGGING
    NLP_SVC --> LOGGING
    TASK_SVC --> MONITORING
    
    style ClientLayer fill:#E3F2FD,stroke:#1976D2,stroke-width:2px
    style APIGateway fill:#F3E5F5,stroke:#7B1FA2,stroke-width:2px
    style CoreServices fill:#E8F5E9,stroke:#388E3C,stroke-width:2px
    style DataLayer fill:#FFF3E0,stroke:#F57C00,stroke-width:2px
    style ExternalIntegrations fill:#FCE4EC,stroke:#C2185B,stroke-width:2px
    style SupportServices fill:#F1F8E9,stroke:#689F38,stroke-width:2px

Container Descriptions

Component Diagram (C4 Level 3)

Voice Processing Service Components

graph TB
    subgraph VPS["Voice Processing Service"]
        AUDIO_IN["Audio Input Handler<br/>WebRTC Stream"]
        CODEC["Codec Manager<br/>Opus/AAC/PCM"]
        DENOISE["Noise Reduction<br/>Spectral Subtraction"]
        VAD["Voice Activity Detection<br/>WebRTC VAD"]
        BUFFER["Audio Buffer<br/>Ring Buffer"]
        QUALITY["Quality Analyzer<br/>SNR/PESQ"]
        AUDIO_OUT["Audio Output Handler<br/>Response Generation"]
    end
    
    AUDIO_IN --> CODEC
    CODEC --> DENOISE
    DENOISE --> VAD
    VAD --> BUFFER
    BUFFER --> QUALITY
    QUALITY --> AUDIO_OUT
    
    style VPS fill:#E8F5E9,stroke:#388E3C,stroke-width:2px

NLP Service Components

graph TB
    subgraph NLP["NLP Service"]
        TOKENIZER["Tokenizer<br/>BERT Tokenization"]
        ENCODER["Encoder<br/>Transformer Model"]
        INTENT["Intent Classifier<br/>Multi-class Classification"]
        ENTITY["Entity Extractor<br/>Named Entity Recognition"]
        CONTEXT["Context Resolver<br/>Coreference Resolution"]
        SEMANTIC["Semantic Analyzer<br/>Dependency Parsing"]
        CONFIDENCE["Confidence Scorer<br/>Uncertainty Quantification"]
    end
    
    TOKENIZER --> ENCODER
    ENCODER --> INTENT
    ENCODER --> ENTITY
    ENCODER --> SEMANTIC
    INTENT --> CONFIDENCE
    ENTITY --> CONTEXT
    SEMANTIC --> CONTEXT
    CONTEXT --> CONFIDENCE
    
    style NLP fill:#E8F5E9,stroke:#388E3C,stroke-width:2px

Task Orchestration Service Components

graph TB
    subgraph TOS["Task Orchestration Service"]
        PARSER["Command Parser<br/>AST Generation"]
        VALIDATOR["Validator<br/>Syntax & Semantic Check"]
        PLANNER["Task Planner<br/>Execution Plan"]
        EXECUTOR["Executor<br/>Task Execution Engine"]
        STATE["State Manager<br/>Workflow State"]
        RETRY["Retry Manager<br/>Error Handling"]
        FEEDBACK["Feedback Generator<br/>Response Formatting"]
    end
    
    PARSER --> VALIDATOR
    VALIDATOR --> PLANNER
    PLANNER --> EXECUTOR
    EXECUTOR --> STATE
    STATE --> RETRY
    RETRY --> FEEDBACK
    
    style TOS fill:#E8F5E9,stroke:#388E3C,stroke-width:2px

Integration Service Components

graph TB
    subgraph IS["Integration Service"]
        ADAPTER_MGR["Adapter Manager<br/>Plugin Registry"]
        IDE_COMP["IDE Component<br/>Code Generation"]
        GIT_COMP["Git Component<br/>Repository Ops"]
        JUPYTER_COMP["Jupyter Component<br/>Notebook Ops"]
        JIRA_COMP["Jira Component<br/>Issue Management"]
        CLOUD_COMP["Cloud Component<br/>Infrastructure Ops"]
        CACHE_LAYER["Cache Layer<br/>API Response Cache"]
    end
    
    ADAPTER_MGR --> IDE_COMP
    ADAPTER_MGR --> GIT_COMP
    ADAPTER_MGR --> JUPYTER_COMP
    ADAPTER_MGR --> JIRA_COMP
    ADAPTER_MGR --> CLOUD_COMP
    IDE_COMP --> CACHE_LAYER
    GIT_COMP --> CACHE_LAYER
    JUPYTER_COMP --> CACHE_LAYER
    JIRA_COMP --> CACHE_LAYER
    CLOUD_COMP --> CACHE_LAYER
    
    style IS fill:#E8F5E9,stroke:#388E3C,stroke-width:2px

Context Management Service Components

graph TB
    subgraph CMS["Context Management Service"]
        SESSION["Session Manager<br/>User Sessions"]
        HISTORY["History Manager<br/>Conversation History"]
        PROJECT["Project Context<br/>Active Projects"]
        MEMORY["Memory Manager<br/>Short-term Memory"]
        PERSISTENCE["Persistence Layer<br/>State Persistence"]
        CLEANUP["Cleanup Manager<br/>TTL Management"]
    end
    
    SESSION --> HISTORY
    SESSION --> PROJECT
    SESSION --> MEMORY
    HISTORY --> PERSISTENCE
    PROJECT --> PERSISTENCE
    MEMORY --> PERSISTENCE
    PERSISTENCE --> CLEANUP
    
    style CMS fill:#E8F5E9,stroke:#388E3C,stroke-width:2px

Deployment Diagram

Production Infrastructure

graph TB
    subgraph CDN["🌐 CDN Layer"]
        CLOUDFLARE["Cloudflare CDN<br/>Global Distribution"]
    end
    
    subgraph LB_ZONE["Load Balancing Zone"]
        ALB["Application Load Balancer<br/>AWS ALB/GCP LB"]
        WAF["Web Application Firewall<br/>DDoS Protection"]
    end
    
    subgraph K8S["☸️ Kubernetes Cluster<br/>Multi-AZ Deployment"]
        subgraph NS_API["API Namespace"]
            APIGW_POD["API Gateway Pod<br/>Replicas: 3"]
            VOICE_POD["Voice Service Pod<br/>Replicas: 5"]
            NLP_POD["NLP Service Pod<br/>Replicas: 4<br/>GPU Nodes"]
            TASK_POD["Task Service Pod<br/>Replicas: 3"]
            CONTEXT_POD["Context Service Pod<br/>Replicas: 3"]
            INTEG_POD["Integration Service Pod<br/>Replicas: 3"]
        end
        
        subgraph NS_DATA["Data Namespace"]
            POSTGRES_POD["PostgreSQL StatefulSet<br/>Primary + 2 Replicas"]
            REDIS_POD["Redis Cluster<br/>6 Nodes"]
            MONGO_POD["MongoDB ReplicaSet<br/>3 Nodes"]
        end
        
        subgraph NS_SUPPORT["Support Namespace"]
            QUEUE_POD["Message Queue<br/>RabbitMQ Cluster"]
            LOGGING_POD["Logging Stack<br/>ELK"]
            MONITOR_POD["Monitoring Stack<br/>Prometheus/Grafana"]
        end
        
        subgraph NS_INGRESS["Ingress"]
            INGRESS["Ingress Controller<br/>Nginx Ingress"]
        end
    end
    
    subgraph STORAGE["💾 Storage Layer"]
        EBS["EBS Volumes<br/>Database Storage"]
        S3["S3/GCS Buckets<br/>Logs & Backups"]
        VECTOR_MANAGED["Managed Vector DB<br/>Pinecone/Weaviate"]
    end
    
    subgraph BACKUP["🔄 Backup & DR"]
        BACKUP_VAULT["Backup Vault<br/>Cross-region"]
        DR_CLUSTER["DR Cluster<br/>Standby"]
    end
    
    subgraph MONITORING["📊 Monitoring & Logging"]
        DATADOG["Datadog<br/>APM & Monitoring"]
        SPLUNK["Splunk<br/>Log Analysis"]
        PAGERDUTY["PagerDuty<br/>Incident Management"]
    end
    
    %% Traffic Flow
    CLOUDFLARE --> WAF
    WAF --> ALB
    ALB --> INGRESS
    
    %% Ingress to Services
    INGRESS --> APIGW_POD
    APIGW_POD --> VOICE_POD
    APIGW_POD --> NLP_POD
    APIGW_POD --> TASK_POD
    APIGW_POD --> CONTEXT_POD
    APIGW_POD --> INTEG_POD
    
    %% Services to Data
    VOICE_POD --> REDIS_POD
    NLP_POD --> VECTOR_MANAGED
    TASK_POD --> POSTGRES_POD
    CONTEXT_POD --> MONGO_POD
    CONTEXT_POD --> REDIS_POD
    INTEG_POD --> POSTGRES_POD
    
    %% Services to Queue
    TASK_POD --> QUEUE_POD
    INTEG_POD --> QUEUE_POD
    
    %% Data to Storage
    POSTGRES_POD --> EBS
    REDIS_POD --> EBS
    MONGO_POD --> EBS
    
    %% Logging & Monitoring
    VOICE_POD --> LOGGING_POD
    NLP_POD --> LOGGING_POD
    TASK_POD --> LOGGING_POD
    LOGGING_POD --> SPLUNK
    LOGGING_POD --> S3
    
    APIGW_POD --> MONITOR_POD
    VOICE_POD --> MONITOR_POD
    MONITOR_POD --> DATADOG
    DATADOG --> PAGERDUTY
    
    %% Backup
    POSTGRES_POD --> BACKUP_VAULT
    MONGO_POD --> BACKUP_VAULT
    BACKUP_VAULT --> DR_CLUSTER
    
    style CDN fill:#FFE0B2,stroke:#E65100,stroke-width:2px
    style LB_ZONE fill:#F3E5F5,stroke:#7B1FA2,stroke-width:2px
    style K8S fill:#E8F5E9,stroke:#388E3C,stroke-width:2px
    style NS_API fill:#E3F2FD,stroke:#1976D2,stroke-width:1px
    style NS_DATA fill:#FFF3E0,stroke:#F57C00,stroke-width:1px
    style NS_SUPPORT fill:#F1F8E9,stroke:#689F38,stroke-width:1px
    style STORAGE fill:#FCE4EC,stroke:#C2185B,stroke-width:2px
    style BACKUP fill:#E0F2F1,stroke:#00796B,stroke-width:2px
    style MONITORING fill:#F1F8E9,stroke:#689F38,stroke-width:2px

Deployment Specifications

graph LR
    subgraph REGIONS["Multi-Region Strategy"]
        PRIMARY["Primary Region<br/>us-east-1<br/>Active-Active"]
        SECONDARY["Secondary Region<br/>eu-west-1<br/>Active-Active"]
        TERTIARY["Tertiary Region<br/>ap-southeast-1<br/>Read-Only"]
    end
    
    subgraph HA["High Availability"]
        AZ1["Availability Zone 1"]
        AZ2["Availability Zone 2"]
        AZ3["Availability Zone 3"]
    end
    
    PRIMARY --> AZ1
    PRIMARY --> AZ2
    PRIMARY --> AZ3
    SECONDARY --> AZ1
    SECONDARY --> AZ2
    
    style REGIONS fill:#E3F2FD,stroke:#1976D2,stroke-width:2px
    style HA fill:#E8F5E9,stroke:#388E3C,stroke-width:2px

Technology Stack Summary

Frontend Technologies

Backend Technologies

Data Storage Technologies

Infrastructure & DevOps

Monitoring & Observability

AI/ML Technologies

Architecture Decision Records

ADR-001: Microservices Architecture

ID: ADR-001
Status: Accepted
Date: 2024-01-15

Context

The system needs to handle multiple concurrent voice processing requests, NLP analysis, and external integrations with varying latency requirements. A monolithic architecture would create bottlenecks and limit independent scaling.

Decision

Adopt a microservices architecture with the following services:

• Voice Processing Service (Python/FastAPI)
• NLP Service (Python/FastAPI with GPU support)
• Task Orchestration Service (Node.js/Express)
• Context Management Service (Node.js/Express)
• Integration Service (Python/FastAPI)

Consequences

Positive:

• Independent scaling of compute-intensive services (NLP)
• Technology diversity (Python for ML, Node.js for orchestration)
• Fault isolation and resilience
• Faster deployment cycles
• Team autonomy

Negative:

• Increased operational complexity
• Network latency between services
• Distributed transaction management
• Debugging complexity

Mitigation:

• Implement service mesh (Istio) for observability
• Use correlation IDs for request tracing
• Implement circuit breakers and retry logic
• Comprehensive monitoring and logging

ADR-002: Kubernetes for Container Orchestration

ID: ADR-002
Status: Accepted
Date: 2024-01-15

Context

The system requires:

• Auto-scaling based on voice processing load
• Self-healing capabilities
• Rolling deployments with zero downtime
• Multi-region deployment support
• 99.9% uptime SLA

Decision

Deploy all microservices on Kubernetes (EKS/GKE/AKS) with:

• Multi-AZ deployment for high availability
• Horizontal Pod Autoscaling (HPA) based on CPU/memory
• StatefulSets for databases
• Helm charts for package management

Consequences

Positive:

• Native auto-scaling and self-healing
• Declarative infrastructure
• Multi-cloud portability
• Mature ecosystem and tooling
• Community support

Negative:

• Steep learning curve
• Operational overhead
• Resource overhead (etcd, control plane)
• Complexity in debugging

Mitigation:

• Use managed Kubernetes services (EKS/GKE)
• Implement comprehensive monitoring
• Automate deployment with GitOps (ArgoCD)
• Regular training for operations team

ADR-003: PostgreSQL + MongoDB Polyglot Persistence

ID: ADR-003
Status: Accepted
Date: 2024-01-16

Context

Different data types have different requirements:

• Structured, transactional data (tasks, users, projects)
• Unstructured conversation logs with flexible schema
• Time-series metrics data
• Vector embeddings for semantic search

Decision

Implement polyglot persistence:

• PostgreSQL: Transactional data, ACID compliance
• MongoDB: Conversation logs, flexible schema
• Redis: Caching, sessions, real-time data
• Pinecone/Weaviate: Vector embeddings

Consequences

Positive:

• Optimal data model for each use case
• Better performance for specific queries
• Flexibility in schema evolution
• Reduced data transformation overhead

Negative:

• Operational complexity
• Multiple backup/recovery procedures
• Data consistency challenges
• Increased infrastructure costs

Mitigation:

• Implement event sourcing for consistency
• Use distributed transactions where needed
• Comprehensive backup strategy
• Clear data ownership boundaries

ADR-004: Real-time Voice Processing with <500ms Latency

ID: ADR-004
Status: Accepted
Date: 2024-01-16

Context

User experience requires voice response within 500ms. This includes:

• Audio capture and transmission
• Voice processing
• NLP analysis
• Task execution
• Response generation

Decision

Implement multi-level optimization:

1. Client-side: WebRTC for low-latency audio streaming
2. Network: Edge computing for voice processing
3. Processing: Streaming NLP with incremental results
4. Caching: Aggressive caching of common intents

Consequences

Positive:

• Acceptable user experience
• Reduced server load
• Better resource utilization
• Improved responsiveness

Negative:

• Complex distributed processing
• Increased client-side complexity
• Cache invalidation challenges
• Requires edge infrastructure

Mitigation:

• Implement SLA monitoring
• Use CDN for edge processing
• Implement request prioritization
• Regular performance testing

ADR-005: Event-Driven Architecture with Message Queue

ID: ADR-005
Status: Accepted
Date: 2024-01-17

Context

Services need to communicate asynchronously for:

• Long-running tasks (code generation, data analysis)
• Decoupling services
• Handling traffic spikes
• Ensuring reliability

Decision

Implement event-driven architecture using RabbitMQ:

• Task events published to message queue
• Services subscribe to relevant events
• Dead-letter queues for failed messages
• Event sourcing for audit trail

Consequences

Positive:

• Loose coupling between services
• Better scalability
• Improved reliability
• Audit trail of all events

Negative:

• Eventual consistency challenges
• Debugging complexity
• Message ordering issues
• Operational overhead

Mitigation:

• Implement idempotent message handlers
• Use correlation IDs for tracing
• Implement monitoring for queue depth
• Regular testing of failure scenarios

ADR-006: OAuth 2.0 + JWT for Authentication

ID: ADR-006
Status: Accepted
Date: 2024-01-17

Context

System needs to support:

• Multiple user types (developers, data scientists, managers)
• Integration with enterprise identity providers
• Stateless authentication
• Fine-grained authorization

Decision

Implement OAuth 2.0 with JWT tokens:

• Auth0/Keycloak as identity provider
• JWT tokens for stateless authentication
• Role-Based Access Control (RBAC)
• Refresh token rotation

Consequences

Positive:

• Industry standard authentication
• Stateless and scalable
• Enterprise integration support
• Fine-grained access control

Negative:

• Token management complexity
• Revocation challenges
• Increased token size in requests
• Requires secure token storage

Mitigation:

• Implement token refresh strategy
• Use short-lived access tokens
• Implement token revocation list
• Secure token storage in client

ADR-007: GPU-Accelerated NLP Processing

ID: ADR-007
Status: Accepted
Date: 2024-01-18

Context

NLP models (BERT, GPT) require significant compute resources. CPU-only processing would exceed latency requirements and increase costs.

Decision

Deploy NLP service on GPU nodes:

• NVIDIA GPUs (A100/H100) for inference
• TensorFlow Serving or Triton for model serving
• Batch processing for non-real-time tasks
• Model quantization for reduced memory

Consequences

Positive:

• 10-100x performance improvement
• Reduced latency for NLP tasks
• Support for larger models
• Better resource utilization

Negative:

• Significantly higher infrastructure costs
• GPU availability constraints
• Increased operational complexity
• Power consumption concerns

Mitigation:

• Use spot instances for non-critical workloads
• Implement model caching
• Use model quantization
• Regular cost optimization

ADR-008: Multi-Region Active-Active Deployment

ID: ADR-008
Status: Accepted
Date: 2024-01-18

Context

99.9% uptime requirement and global user base require:

• Disaster recovery capability
• Low-latency access from multiple regions
• Automatic failover

Decision

Deploy active-active in multiple regions:

• Primary: us-east-1 (North America)
• Secondary: eu-west-1 (Europe)
• Tertiary: ap-southeast-1 (Asia-Pacific)
• Global load balancing with health checks
• Cross-region replication for data

Consequences

Positive:

• Reduced latency for global users
• Automatic failover capability
• Improved disaster recovery
• Better compliance with data residency

Negative:

• 3x infrastructure costs
• Data consistency challenges
• Operational complexity
• Network bandwidth costs

Mitigation:

• Use managed services for replication
• Implement eventual consistency patterns
• Comprehensive monitoring across regions
• Regular DR testing

ADR-009: Service Mesh (Istio) for Observability

ID: ADR-009
Status: Accepted
Date: 2024-01-19

Context

Microservices architecture requires:

• Distributed tracing
• Service-to-service communication management
• Traffic management and routing
• Security policies

Decision

Implement Istio service mesh:

• Automatic sidecar injection
• Distributed tracing with Jaeger
• Traffic management with VirtualServices
• mTLS for service-to-service communication

Consequences

Positive:

• Transparent observability
• Automatic mTLS
• Advanced traffic management
• Reduced application code complexity

Negative:

• Additional resource overhead (sidecars)
• Increased latency (minimal)
• Operational complexity
• Debugging challenges

Mitigation:

• Use managed Istio (AWS App Mesh, GCP Anthos)
• Implement comprehensive monitoring
• Regular training for operations team
• Gradual rollout

ADR-010: Infrastructure as Code with Terraform

ID: ADR-010
Status: Accepted
Date: 2024-01-19

Context

Multi-region, multi-cloud deployment requires:

• Reproducible infrastructure
• Version control for infrastructure
• Automated provisioning
• Disaster recovery capability

Decision

Use Terraform for infrastructure provisioning:

• Modular Terraform configurations
• State management with remote backend (S3/GCS)
• Automated testing with Terratest
• GitOps workflow for changes

Consequences

Positive:

• Infrastructure as code benefits
• Version control and audit trail
• Reproducible deployments
• Multi-cloud portability

Negative:

• Learning curve for team
• State management complexity
• Terraform limitations for some resources
• Requires discipline in code organization

Mitigation:

• Implement code review process
• Use Terraform modules for reusability
• Implement state locking
• Regular training and documentation

Data Flow Architecture

Voice Command Processing Flow

sequenceDiagram
    participant User as 👤 User
    participant Client as 🖥️ Web Client
    participant VoiceAPI as 🎤 Voice API
    participant VoiceSvc as Voice Service
    participant NLPSvc as NLP Service
    participant TaskSvc as Task Service
    participant IntegSvc as Integration Service
    participant ExtSys as 🔌 External System
    
    User->>Client: Speaks voice command
    Client->>VoiceAPI: WebRTC audio stream
    VoiceAPI->>VoiceSvc: Audio chunks
    
    par Voice Processing
        VoiceSvc->>VoiceSvc: Noise reduction
        VoiceSvc->>VoiceSvc: Voice activity detection
        VoiceSvc->>VoiceSvc: Audio codec handling
    end
    
    VoiceSvc->>NLPSvc: Transcribed text
    
    par NLP Analysis
        NLPSvc->>NLPSvc: Tokenization
        NLPSvc->>NLPSvc: Intent classification
        NLPSvc->>NLPSvc: Entity extraction
    end
    
    NLPSvc->>TaskSvc: Parsed intent + entities
    TaskSvc->>TaskSvc: Validate command
    TaskSvc->>TaskSvc: Generate execution plan
    
    alt External Integration Required
        TaskSvc->>IntegSvc: Execute integration task
        IntegSvc->>ExtSys: API call
        ExtSys-->>IntegSvc: Response
        IntegSvc-->>TaskSvc: Result
    end
    
    TaskSvc->>TaskSvc: Generate response
    TaskSvc->>VoiceAPI: Response text
    VoiceAPI->>VoiceAPI: Text-to-speech synthesis
    VoiceAPI->>Client: Audio response
    Client->>User: Play audio response

Data Persistence Flow

sequenceDiagram
    participant Service as Microservice
    participant Cache as Redis Cache
    participant Queue as Message Queue
    participant PrimaryDB as PostgreSQL
    participant ReplicaDB as PostgreSQL Replica
    participant DocStore as MongoDB
    participant VectorDB as Vector Database
    
    Service->>Cache: Check cache
    alt Cache Hit
        Cache-->>Service: Return cached data
    else Cache Miss
        Service->>PrimaryDB: Query data
        PrimaryDB-->>Service: Return data
        Service->>Cache: Store in cache
    end
    
    Service->>Queue: Publish event
    Queue->>DocStore: Store event log
    Queue->>VectorDB: Update embeddings
    
    PrimaryDB->>ReplicaDB: Replication stream
    
    Service->>PrimaryDB: Write transaction
    PrimaryDB->>PrimaryDB: Commit
    PrimaryDB->>ReplicaDB: Async replication

Integration Workflow

graph TD
    A["Voice Command<br/>Generate Code"] --> B["Parse Intent"]
    B --> C["Validate Syntax"]
    C --> D["Check Permissions"]
    D --> E{Integration Type?}
    
    E -->|IDE| F["IDE Adapter"]
    E -->|Git| G["Git Adapter"]
    E -->|Jupyter| H["Jupyter Adapter"]
    E -->|Jira| I["Jira Adapter"]
    E -->|Cloud| J["Cloud Adapter"]
    
    F --> K["Execute Integration"]
    G --> K
    H --> K
    I --> K
    J --> K
    
    K --> L["Cache Result"]
    L --> M["Generate Response"]
    M --> N["Text-to-Speech"]
    N --> O["Return to User"]
    
    style A fill:#E3F2FD,stroke:#1976D2
    style K fill:#E8F5E9,stroke:#388E3C
    style O fill:#FFF3E0,stroke:#F57C00

Security Architecture

Authentication & Authorization Flow

graph TB
    subgraph AuthFlow["Authentication Flow"]
        A["User Login"]
        B["OAuth 2.0 Provider<br/>Auth0/Keycloak"]
        C["JWT Token Generation"]
        D["Refresh Token Storage"]
    end
    
    subgraph AuthzFlow["Authorization Flow"]
        E["API Request + JWT"]
        F["Token Validation"]
        G["RBAC Check"]
        H["Resource Access"]
    end
    
    subgraph MFA["Multi-Factor Authentication"]
        I["TOTP/SMS"]
        J["Biometric"]
        K["Hardware Key"]
    end
    
    A --> B
    B --> C
    C --> D
    E --> F
    F --> G
    G --> H
    B --> I
    B --> J
    B --> K
    
    style AuthFlow fill:#E3F2FD,stroke:#1976D2,stroke-width:2px
    style AuthzFlow fill:#E8F5E9,stroke:#388E3C,stroke-width:2px
    style MFA fill:#FCE4EC,stroke:#C2185B,stroke-width:2px

Data Security Architecture

graph TB
    subgraph Encryption["🔐 Encryption"]
        TLS["TLS 1.3<br/>In-Transit"]
        AES["AES-256<br/>At-Rest"]
        FIELD["Field-Level<br/>Encryption"]
    end
    
    subgraph Access["🔑 Access Control"]
        RBAC["Role-Based<br/>Access Control"]
        ABAC["Attribute-Based<br/>Access Control"]
        AUDIT["Audit Logging"]
    end
    
    subgraph Privacy["🛡️ Privacy"]
        PII["PII Masking"]
        GDPR["GDPR Compliance"]
        RETENTION["Data Retention"]
    end
    
    subgraph Secrets["🔓 Secrets Management"]
        VAULT["HashiCorp Vault"]
        ROTATION["Key Rotation"]
        MONITORING["Access Monitoring"]
    end
    
    style Encryption fill:#FFEBEE,stroke:#C62828,stroke-width:2px
    style Access fill:#F3E5F5,stroke:#7B1FA2,stroke-width:2px
    style Privacy fill:#E0F2F1,stroke:#00796B,stroke-width:2px
    style Secrets fill:#FFF3E0,stroke:#F57C00,stroke-width:2px

Network Security

graph TB
    subgraph Internet["Internet"]
        USER["Users"]
    end
    
    subgraph DMZ["DMZ"]
        WAF["Web Application<br/>Firewall"]
        DDOS["DDoS Protection<br/>Cloudflare"]
    end
    
    subgraph Network["Network Layer"]
        VPC["VPC<br/>Private Network"]
        NSG["Network Security<br/>Groups"]
        NACL["Network ACLs"]
    end
    
    subgraph ServiceMesh["Service Mesh"]
        MTLS["mTLS<br/>Service-to-Service"]
        POLICIES["Network Policies"]
    end
    
    subgraph Database["Database Layer"]
        ENCRYPTION["Encryption at Rest"]
        BACKUP["Encrypted Backups"]
    end
    
    USER --> DDOS
    DDOS --> WAF
    WAF --> VPC
    VPC --> NSG
    NSG --> NACL
    NACL --> MTLS
    MTLS --> POLICIES
    POLICIES --> ENCRYPTION
    ENCRYPTION --> BACKUP
    
    style Internet fill:#FFEBEE,stroke:#C62828,stroke-width:2px
    style DMZ fill:#FFF3E0,stroke:#F57C00,stroke-width:2px
    style Network fill:#E3F2FD,stroke:#1976D2,stroke-width:2px
    style ServiceMesh fill:#E8F5E9,stroke:#388E3C,stroke-width:2px
    style Database fill:#F3E5F5,stroke:#7B1FA2,stroke-width:2px

Integration Architecture

External System Integration Points

graph TB
    subgraph VoiceAgent["Voice Agent Core"]
        INTEG["Integration Service"]
    end
    
    subgraph IDEIntegration["IDE Integration"]
        VSC["VS Code Extension<br/>Language Server Protocol"]
        JB["JetBrains Plugin<br/>IDE Plugin API"]
        SUBLIME["Sublime Text Plugin<br/>Plugin API"]
    end
    
    subgraph GitIntegration["Git Integration"]
        GITHUB["GitHub API<br/>REST + GraphQL"]
        GITLAB["GitLab API<br/>REST + GraphQL"]
        BITBUCKET["Bitbucket API<br/>REST"]
    end
    
    subgraph DataIntegration["Data Science Integration"]
        JUPYTER["Jupyter API<br/>Notebook Protocol"]
        COLAB["Google Colab<br/>REST API"]
        KAGGLE["Kaggle API<br/>REST"]
    end
    
    subgraph ProjectIntegration["Project Management"]
        JIRA["Jira API<br/>REST"]
        ASANA["Asana API<br/>REST"]
        MONDAY["Monday.com API<br/>GraphQL"]
    end
    
    subgraph CloudIntegration["Cloud Platforms"]
        AWS["AWS SDK<br/>Boto3"]
        GCP["Google Cloud SDK<br/>Python"]
        AZURE["Azure SDK<br/>Python"]
    end
    
    subgraph CommIntegration["Communication"]
        SLACK["Slack API<br/>WebSocket"]
        TEAMS["Teams API<br/>REST"]
        DISCORD["Discord API<br/>WebSocket"]
    end
    
    INTEG --> VSC
    INTEG --> JB
    INTEG --> SUBLIME
    INTEG --> GITHUB
    INTEG --> GITLAB
    INTEG --> BITBUCKET
    INTEG --> JUPYTER
    INTEG --> COLAB
    INTEG --> KAGGLE
    INTEG --> JIRA
    INTEG --> ASANA
    INTEG --> MONDAY
    INTEG --> AWS
    INTEG --> GCP
    INTEG --> AZURE
    INTEG --> SLACK
    INTEG --> TEAMS
    INTEG --> DISCORD
    
    style VoiceAgent fill:#E3F2FD,stroke:#1976D2,stroke-width:2px
    style IDEIntegration fill:#E8F5E9,stroke:#388E3C,stroke-width:2px
    style GitIntegration fill:#FFF3E0,stroke:#F57C00,stroke-width:2px
    style DataIntegration fill:#FCE4EC,stroke:#C2185B,stroke-width:2px
    style ProjectIntegration fill:#F3E5F5,stroke:#7B1FA2,stroke-width:2px
    style CloudIntegration fill:#E0F2F1,stroke:#00796B,stroke-width:2px
    style CommIntegration fill:#F1F8E9,stroke:#689F38,stroke-width:2px

API Integration Patterns

graph TB
    subgraph Patterns["Integration Patterns"]
        REST["REST API<br/>Synchronous<br/>Request-Response"]
        GRAPHQL["GraphQL API<br/>Query Language<br/>Flexible Queries"]
        WEBHOOK["Webhooks<br/>Event-Driven<br/>Async Notifications"]
        GRPC["gRPC<br/>High Performance<br/>Binary Protocol"]
        WEBSOCKET["WebSocket<br/>Real-time<br/>Bidirectional"]
    end
    
    subgraph Adapters["Adapter Layer"]
        ADAPTER1["REST Adapter"]
        ADAPTER2["GraphQL Adapter"]
        ADAPTER3["Webhook Adapter"]
        ADAPTER4["gRPC Adapter"]
        ADAPTER5["WebSocket Adapter"]
    end
    
    subgraph Cache["Caching Strategy"]
        REDIS_CACHE["Redis Cache<br/>API Responses"]
        INVALIDATION["Cache Invalidation<br/>TTL + Events"]
    end
    
    REST --> ADAPTER1
    GRAPHQL --> ADAPTER2
    WEBHOOK --> ADAPTER3
    GRPC --> ADAPTER4
    WEBSOCKET --> ADAPTER5
    
    ADAPTER1 --> REDIS_CACHE
    ADAPTER2 --> REDIS_CACHE
    ADAPTER3 --> REDIS_CACHE
    ADAPTER4 --> REDIS_CACHE
    ADAPTER5 --> REDIS_CACHE
    
    REDIS_CACHE --> INVALIDATION
    
    style Patterns fill:#E3F2FD,stroke:#1976D2,stroke-width:2px
    style Adapters fill:#E8F5E9,stroke:#388E3C,stroke-width:2px
    style Cache fill:#FFF3E0,stroke:#F57C00,stroke-width:2px

Functional Requirements

FR-001: Voice Command Processing

ID: FR-001
Priority: P0 (Critical)
Status: Active

Description: System shall capture, process, and interpret voice commands from users with <500ms latency.

Acceptance Criteria:

• Voice input captured via WebRTC with sample rate ≥16kHz
• Audio processed with noise reduction achieving SNR >20dB
• Voice activity detection with >95% accuracy
• End-to-end latency <500ms (p95)
• Support for multiple languages (English, Spanish, French, German, Chinese)
• Offline fallback for critical commands

Traceability: UC-001, TC-001

FR-002: Natural Language Understanding

ID: FR-002
Priority: P0 (Critical)
Status: Active

Description: System shall understand developer intent from natural language commands and extract relevant entities.

Acceptance Criteria:

• Intent classification accuracy >95%
• Entity extraction F1-score >0.90
• Support for 50+ intent types
• Context-aware understanding (project, file, language)
• Ambiguity resolution with clarification prompts
• Support for complex multi-step commands

Traceability: UC-002, TC-002

FR-003: Code Generation

ID: FR-003
Priority: P0 (Critical)
Status: Active

Description: System shall generate code based on voice commands and insert into IDE.

Acceptance Criteria:

• Generate syntactically correct code for 90% of commands
• Support for Python, JavaScript, Java, Go, Rust
• Code follows project style guide
• Generated code includes comments and docstrings
• Integration with VS Code, JetBrains IDEs
• Undo/redo support for generated code

Traceability: UC-003, TC-003

FR-004: Data Analysis Assistance

ID: FR-004
Priority: P0 (Critical)
Status: Active

Description: System shall assist data scientists with analysis tasks through voice commands.

Acceptance Criteria:

• Generate Jupyter notebooks from voice commands
• Execute data analysis workflows
• Visualize results with appropriate charts
• Suggest analysis techniques based on data
• Support for pandas, scikit-learn, TensorFlow
• Export results in multiple formats (CSV, JSON, Parquet)

Traceability: UC-004, TC-004

FR-005: Project Management Integration

ID: FR-005
Priority: P1 (High)
Status: Active

Description: System shall integrate with project management tools for task tracking.

Acceptance Criteria:

• Create/update Jira issues via voice
• Track time spent on tasks
• Generate status reports
• Link code commits to issues
• Support for Jira, Asana, Monday.com
• Bi-directional synchronization

Traceability: UC-005, TC-005

FR-006: Real-time Collaboration

ID: FR-006
Priority: P1 (High)
Status: Active

Description: System shall support real-time collaboration between multiple users.

Acceptance Criteria:

• Multiple users can work on same project simultaneously
• Real-time synchronization of changes
• Conflict resolution for concurrent edits
• Presence awareness (who is online)
• Shared voice chat for team communication
• Activity history and audit trail

Traceability: UC-006, TC-006

FR-007: Security & Access Control

ID: FR-007
Priority: P0 (Critical)
Status: Active

Description: System shall enforce security policies and access control.

Acceptance Criteria:

• OAuth 2.0 authentication with MFA support
• Role-based access control (RBAC)
• Encryption of data in transit (TLS 1.3)
• Encryption of data at rest (AES-256)
• Audit logging of all actions
• Compliance with GDPR, SOC 2, ISO 27001

Traceability: UC-007, TC-007

FR-008: Error Handling & Recovery

ID: FR-008
Priority: P1 (High)
Status: Active

Description: System shall handle errors gracefully and provide recovery options.

Acceptance Criteria:

• Graceful degradation on service failures
• Automatic retry with exponential backoff
• Circuit breaker pattern for external APIs
• User-friendly error messages
• Automatic recovery from transient failures
• Manual recovery options for persistent failures

Traceability: UC-008, TC-008

Use Cases

UC-001: Developer Generates Code via Voice

ID: UC-001
Actor: Software Developer
Priority: P0

Preconditions:

• User is authenticated and authorized
• IDE is open and connected to voice agent
• Project context is loaded

Main Flow:

1. Developer speaks: “Generate a REST API endpoint for user authentication”
2. System captures and processes voice command
3. NLP service identifies intent: “code_generation”
4. System extracts entities: language=Python, type=REST_API, feature=authentication
5. System generates code using LLM
6. Code is inserted into IDE at cursor position
7. System provides voice confirmation: “Generated authentication endpoint”

Alternative Flows:

• A1: User requests modification: “Add error handling”
  • System modifies generated code
  • Provides updated code to IDE
• A2: User rejects code: “Undo”
  • System removes generated code
  • Reverts to previous state

Postconditions:

• Code is inserted into IDE
• User can review and modify code
• Action is logged for audit trail

Acceptance Criteria:

• Code is syntactically correct
• Code follows project conventions
• Latency <2 seconds
• User satisfaction >4/5

UC-002: Data Scientist Analyzes Dataset

ID: UC-002
Actor: Data Scientist
Priority: P0

Preconditions:

• User is authenticated
• Dataset is accessible
• Jupyter environment is available

Main Flow:

1. Data scientist speaks: “Analyze the sales dataset for trends”
2. System captures voice command
3. NLP service identifies intent: “data_analysis”
4. System extracts entities: dataset=sales, analysis_type=trend_analysis
5. System generates Jupyter notebook with analysis code
6. Notebook is executed automatically
7. Results are visualized with appropriate charts
8. System provides voice summary: “Found 15% growth trend in Q4”

Alternative Flows:

• A1: User requests different visualization
  • System regenerates charts with new parameters
• A2: User wants to export results
  • System exports to CSV/JSON/Parquet

Postconditions:

• Analysis results are available in Jupyter
• Visualizations are generated
• Results can be shared with team

Acceptance Criteria:

• Analysis completes within 30 seconds
• Visualizations are accurate
• Results are exportable
• User satisfaction >4/5

UC-003: Project Manager Tracks Progress

ID: UC-003
Actor: Project Manager
Priority: P1

Preconditions:

• User is authenticated
• Project is configured in system
• Team members have logged time

Main Flow:

1. PM speaks: “Show me project status for Q1”
2. System captures voice command
3. NLP service identifies intent: “status_report”
4. System extracts entities: project=Q1, report_type=status
5. System aggregates data from Jira, Git, time tracking
6. System generates comprehensive status report
7. Report is displayed with visualizations
8. System provides voice summary with key metrics

Alternative Flows:

• A1: User requests specific team member status
  • System filters data for that team member
• A2: User wants to export report
  • System exports to PDF/Excel

Postconditions:

• Status report is generated
• Metrics are visualized
• Report can be shared with stakeholders

Acceptance Criteria:

• Report generation <5 seconds
• Data accuracy >99%
• Visualizations are clear
• User satisfaction >4/5

UC-004: DevOps Engineer Deploys Application

ID: UC-004
Actor: DevOps Engineer
Priority: P1

Preconditions:

• User is authenticated with elevated privileges
• Application is built and tested
• Deployment configuration is available

Main Flow:

1. DevOps engineer speaks: “Deploy version 2.1.0 to production”
2. System captures voice command
3. NLP service identifies intent: “deploy”
4. System extracts entities: version=2.1.0, environment=production
5. System validates deployment prerequisites
6. System initiates deployment pipeline
7. System monitors deployment progress
8. System provides voice updates: “Deployment 45% complete”
9. Deployment completes successfully
10. System confirms: “Version 2.1.0 deployed to production”

Alternative Flows:

• A1: Deployment fails
  • System provides error details
  • Offers rollback option
• A2: User requests deployment status
  • System provides real-time status update

Postconditions:

• Application is deployed
• Deployment is logged
• Team is notified

Acceptance Criteria:

• Deployment completes successfully
• Zero-downtime deployment
• Rollback available if needed
• Deployment time <10 minutes

Test Cases

TC-001: Voice Command Processing Latency

ID: TC-001
Related FR: FR-001
Type: Performance Test

Test Steps:

1. Capture voice command: “Generate a function”
2. Measure time from voice input to system response
3. Repeat 100 times with different commands
4. Calculate p50, p95, p99 latencies

Expected Results:

• p50 latency: <300ms
• p95 latency: <500ms
• p99 latency: <1000ms

Pass Criteria: p95 latency <500ms

TC-002: Intent Classification Accuracy

ID: TC-002
Related FR: FR-002
Type: Functional Test

Test Data: 1000 voice commands across 50 intent types

Test Steps:

1. Process each voice command
2. Compare predicted intent with ground truth
3. Calculate accuracy, precision, recall, F1-score

Expected Results:

• Accuracy: >95%
• Precision: >0.94
• Recall: >0.94
• F1-score: >0.94

Pass Criteria: Accuracy >95%

TC-003: Code Generation Correctness

ID: TC-003
Related FR: FR-003
Type: Functional Test

Test Data: 500 code generation requests across 5 languages

Test Steps:

1. Generate code for each request
2. Validate syntax using language-specific linter
3. Execute code to verify functionality
4. Check code style compliance

Expected Results:

• Syntax correctness: >95%
• Functionality correctness: >90%
• Style compliance: >85%

Pass Criteria: Syntax correctness >95%

TC-004: Data Analysis Accuracy

ID: TC-004
Related FR: FR-004
Type: Functional Test

Test Data: 100 datasets with known analysis results

Test Steps:

1. Request analysis for each dataset
2. Compare generated results with expected results
3. Validate visualizations
4. Check statistical accuracy

Expected Results:

• Result accuracy: >98%
• Visualization correctness: >95%
• Statistical validity: >99%

Pass Criteria: Result accuracy >98%

TC-005: Security Access Control

ID: TC-005
Related FR: FR-007
Type: Security Test

Test Steps:

1. Attempt to access resource without authentication
2. Attempt to access resource with invalid token
3. Attempt to access resource with insufficient permissions
4. Attempt to access resource with valid permissions

Expected Results:

• Unauthorized access denied: 100%
• Invalid token rejected: 100%
• Insufficient permissions denied: 100%
• Valid access granted: 100%

Pass Criteria: All unauthorized access denied

TC-006: System Availability

ID: TC-006
Related FR: FR-001
Type: Reliability Test

Test Duration: 30 days continuous operation

Test Steps:

1. Monitor system uptime continuously
2. Simulate failures and recovery
3. Track response times
4. Monitor error rates

Expected Results:

• Uptime: >99.9%
• Error rate: <0.1%
• Recovery time: <5 minutes

Pass Criteria: Uptime >99.9%

TC-007: Concurrent User Load

ID: TC-007
Related FR: FR-001
Type: Load Test

Test Scenario: 1000 concurrent users

Test Steps:

1. Ramp up to 1000 concurrent users
2. Maintain load for 30 minutes
3. Monitor response times, error rates, resource usage
4. Ramp down gradually

Expected Results:

• Response time p95: <1000ms
• Error rate: <0.1%
• CPU utilization: <80%
• Memory utilization: <85%

Pass Criteria: Response time p95 <1000ms, Error rate <0.1%

TC-008: Data Consistency

ID: TC-008
Related FR: FR-006
Type: Functional Test

Test Scenario: Concurrent writes from multiple users

Test Steps:

1. Create 10 concurrent users
2. Each user performs 100 write operations
3. Verify data consistency across all replicas
4. Check for data loss or corruption

Expected Results:

• Data consistency: 100%
• No data loss: 0 records lost
• No corruption: 0 corrupted records

Pass Criteria: Data consistency 100%, No data loss

Monitoring & Observability

Key Performance Indicators (KPIs)

graph TB
    subgraph Latency["⏱️ Latency Metrics"]
        L1["Voice Processing Latency<br/>Target: <500ms p95"]
        L2["NLP Processing Latency<br/>Target: <300ms p95"]
        L3["Task Execution Latency<br/>Target: <1000ms p95"]
        L4["End-to-End Latency<br/>Target: <2000ms p95"]
    end
    
    subgraph Availability["✅ Availability Metrics"]
        A1["System Uptime<br/>Target: 99.9%"]
        A2["Service Availability<br/>Target: 99.95%"]
        A3["Database Availability<br/>Target: 99.99%"]
        A4["Error Rate<br/>Target: <0.1%"]
    end
    
    subgraph Quality["⭐ Quality Metrics"]
        Q1["Intent Classification Accuracy<br/>Target: >95%"]
        Q2["Code Generation Correctness<br/>Target: >95%"]
        Q3["User Satisfaction<br/>Target: >4.5/5"]
        Q4["Feature Adoption<br/>Target: >80%"]
    end
    
    subgraph Resource["📊 Resource Metrics"]
        R1["CPU Utilization<br/>Target: <70%"]
        R2["Memory Utilization<br/>Target: <75%"]
        R3["Network Bandwidth<br/>Target: <80%"]
        R4["Storage Utilization<br/>Target: <85%"]
    end
    
    subgraph Security["🔒 Security Metrics"]
        S1["Authentication Success Rate<br/>Target: >99.9%"]
        S2["Authorization Accuracy<br/>Target: 100%"]
        S3["Encryption Coverage<br/>Target: 100%"]
        S4["Audit Log Completeness<br/>Target: 100%"]
    end
    
    style Latency fill:#E3F2FD,stroke:#1976D2,stroke-width:2px
    style Availability fill:#E8F5E9,stroke:#388E3C,stroke-width:2px
    style Quality fill:#FFF3E0,stroke:#F57C00,stroke-width:2px
    style Resource fill:#FCE4EC,stroke:#C2185B,stroke-width:2px
    style Security fill:#F3E5F5,stroke:#7B1FA2,stroke-width:2px

Monitoring Dashboard

graph TB
    subgraph Dashboard["📈 Monitoring Dashboard"]
        REAL_TIME["Real-time Metrics<br/>Updated every 10s"]
        ALERTS["Alert Management<br/>Critical/Warning/Info"]
        TRENDS["Trend Analysis<br/>24h/7d/30d"]
        REPORTS["Automated Reports<br/>Daily/Weekly/Monthly"]
    end
    
    subgraph DataSources["Data Sources"]
        PROMETHEUS["Prometheus<br/>Metrics Collection"]
        JAEGER["Jaeger<br/>Distributed Tracing"]
        ELK["ELK Stack<br/>Log Aggregation"]
        DATADOG["Datadog<br/>APM"]
    end
    
    subgraph Visualization["Visualization"]
        GRAFANA["Grafana<br/>Dashboards"]
        KIBANA["Kibana<br/>Log Visualization"]
        CUSTOM["Custom Dashboards<br/>React"]
    end
    
    PROMETHEUS --> GRAFANA
    JAEGER --> GRAFANA
    ELK --> KIBANA
    DATADOG --> GRAFANA
    
    GRAFANA --> REAL_TIME
    KIBANA --> REAL_TIME
    REAL_TIME --> ALERTS
    REAL_TIME --> TRENDS
    TRENDS --> REPORTS
    
    style Dashboard fill:#E3F2FD,stroke:#1976D2,stroke-width:2px
    style DataSources fill:#E8F5E9,stroke:#388E3C,stroke-width:2px
    style Visualization fill:#FFF3E0,stroke:#F57C00,stroke-width:2px

Scalability Strategy

Horizontal Scaling

graph TB
    subgraph Current["Current State<br/>100 Users"]
        API1["API Gateway<br/>1 instance"]
        VOICE1["Voice Service<br/>2 instances"]
        NLP1["NLP Service<br/>2 instances"]
        TASK1["Task Service<br/>2 instances"]
    end
    
    subgraph Scaled["Scaled State<br/>10,000 Users"]
        API2["API Gateway<br/>10 instances"]
        VOICE2["Voice Service<br/>50 instances"]
        NLP2["NLP Service<br/>40 instances<br/>GPU Nodes"]
        TASK2["Task Service<br/>30 instances"]
    end
    
    subgraph Metrics["Scaling Triggers"]
        CPU["CPU >70%"]
        MEMORY["Memory >75%"]
        LATENCY["Latency >1000ms"]
        QUEUE["Queue Depth >1000"]
    end
    
    Current --> Metrics
    Metrics --> Scaled
    
    style Current fill:#E3F2FD,stroke:#1976D2,stroke-width:2px
    style Scaled fill:#E8F5E9,stroke:#388E3C,stroke-width:2px
    style Metrics fill:#FFF3E0,stroke:#F57C00,stroke-width:2px

Database Scaling

graph TB
    subgraph ReadScaling["Read Scaling"]
        PRIMARY["Primary DB<br/>Write Operations"]
        REPLICA1["Read Replica 1<br/>us-east-1"]
        REPLICA2["Read Replica 2<br/>eu-west-1"]
        REPLICA3["Read Replica 3<br/>ap-southeast-1"]
    end
    
    subgraph WriteScaling["Write Scaling"]
        SHARDING["Database Sharding<br/>by User ID"]
        SHARD1["Shard 1<br/>Users 0-25%"]
        SHARD2["Shard 2<br/>Users 25-50%"]
        SHARD3["Shard 3<br/>Users 50-75%"]
        SHARD4["Shard 4<br/>Users 75-100%"]
    end
    
    PRIMARY --> REPLICA1
    PRIMARY --> REPLICA2
    PRIMARY --> REPLICA3
    
    SHARDING --> SHARD1
    SHARDING --> SHARD2
    SHARDING --> SHARD3
    SHARDING --> SHARD4
    
    style ReadScaling fill:#E3F2FD,stroke:#1976D2,stroke-width:2px
    style WriteScaling fill:#E8F5E9,stroke:#388E3C,stroke-width:2px

Disaster Recovery & Business Continuity

Backup Strategy

graph TB
    subgraph BackupFrequency["Backup Frequency"]
        HOURLY["Hourly Backups<br/>Last 24 hours"]
        DAILY["Daily Backups<br/>Last 30 days"]
        WEEKLY["Weekly Backups<br/>Last 90 days"]
        MONTHLY["Monthly Backups<br/>Last 1 year"]
    end
    
    subgraph BackupLocation["Backup Location"]
        PRIMARY_REGION["Primary Region<br/>us-east-1"]
        SECONDARY_REGION["Secondary Region<br/>eu-west-1"]
        TERTIARY_REGION["Tertiary Region<br/>ap-southeast-1"]
        COLD_STORAGE["Cold Storage<br/>Glacier"]
    end
    
    subgraph Recovery["Recovery Options"]
        RTO["RTO: 15 minutes<br/>Recovery Time Objective"]
        RPO["RPO: 1 hour<br/>Recovery Point Objective"]
        FAILOVER["Automatic Failover<br/>on Primary Failure"]
    end
    
    HOURLY --> PRIMARY_REGION
    DAILY --> SECONDARY_REGION
    WEEKLY --> TERTIARY_REGION
    MONTHLY --> COLD_STORAGE
    
    PRIMARY_REGION --> RTO
    SECONDARY_REGION --> RPO
    TERTIARY_REGION --> FAILOVER
    
    style BackupFrequency fill:#E3F2FD,stroke:#1976D2,stroke-width:2px
    style BackupLocation fill:#E8F5E9,stroke:#388E3C,stroke-width:2px
    style Recovery fill:#FFF3E0,stroke:#F57C00,stroke-width:2px

Disaster Recovery Plan

stateDiagram-v2
    [*] --> Normal: System Operating
    
    Normal --> Degraded: Partial Failure
    Normal --> Failed: Complete Failure
    
    Degraded --> Normal: Auto-Recovery
    Degraded --> Failed: Cascading Failure
    
    Failed --> Recovery: Initiate DR
    
    Recovery --> Failover: Activate Standby
    Failover --> Restored: Services Restored
    Restored --> Validation: Validate Data
    Validation --> Normal: Resume Operations
    
    note right of Normal
        99.9% Uptime
        All Services Active
    end note
    
    note right of Degraded
        Reduced Capacity
        Some Services Down
        Auto-scaling Active
    end note
    
    note right of Failed
        Complete Outage
        DR Activated
        RTO: 15 minutes
    end note
    
    note right of Recovery
        Failover in Progress
        Data Sync Active
        Monitoring Alerts
    end note
    
    note right of Restored
        Services Online
        Data Validated
        Performance Monitored
    end note

Cost Optimization

Resource Optimization

graph TB
    subgraph Optimization["Cost Optimization Strategies"]
        SPOT["Spot Instances<br/>60% cost savings<br/>Non-critical workloads"]
        RESERVED["Reserved Instances<br/>40% cost savings<br/>Baseline capacity"]
        AUTOSCALE["Auto-scaling<br/>Pay for actual usage<br/>Peak demand handling"]
        CACHE["Caching Strategy<br/>Reduce API calls<br/>Lower bandwidth"]
    end
    
    subgraph Monitoring["Cost Monitoring"]
        BUDGET["Budget Alerts<br/>Monthly limits"]
        ANOMALY["Anomaly Detection<br/>Unusual spending"]
        OPTIMIZATION["Optimization Recommendations<br/>ML-based"]
    end
    
    subgraph Savings["Estimated Savings"]
        COMPUTE["Compute: 45%"]
        STORAGE["Storage: 30%"]
        NETWORK["Network: 25%"]
        TOTAL["Total: 35%"]
    end
    
    SPOT --> MONITORING
    RESERVED --> MONITORING
    AUTOSCALE --> MONITORING
    CACHE --> MONITORING
    
    MONITORING --> SAVINGS
    
    style Optimization fill:#E3F2FD,stroke:#1976D2,stroke-width:2px
    style Monitoring fill:#E8F5E9,stroke:#388E3C,stroke-width:2px
    style Savings fill:#FFF3E0,stroke:#F57C00,stroke-width:2px

Roadmap & Future Enhancements

Phase 1: MVP (Q1 2024)

• ✅ Voice command processing
• ✅ Basic NLP intent recognition
• ✅ Code generation for Python/JavaScript
• ✅ IDE integration (VS Code)
• ✅ Basic authentication

Phase 2: Enhanced Features (Q2 2024)

• 🔄 Multi-language support (5+ languages)
• 🔄 Advanced NLP with context awareness
• 🔄 Data science integration (Jupyter)
• 🔄 Project management integration (Jira)
• 🔄 Real-time collaboration

Phase 3: Enterprise Features (Q3 2024)

• 📋 Advanced security (MFA, SSO)
• 📋 Compliance certifications (SOC 2, GDPR)
• 📋 Multi-region deployment
• 📋 Advanced analytics and reporting
• 📋 Custom integrations framework

Phase 4: AI Enhancements (Q4 2024)

• 🚀 GPT-4 integration
• 🚀 Custom model fine-tuning
• 🚀 Predictive code suggestions
• 🚀 Automated testing generation
• 🚀 Performance optimization recommendations

Conclusion

This comprehensive architecture documentation provides a complete blueprint for the Voice-Centric Software Development and Data Science Agent system. The design emphasizes:

1. Scalability: Microservices architecture with horizontal scaling capabilities
2. Performance: <500ms latency for voice processing with edge computing
3. Reliability: 99.9% uptime with multi-region active-active deployment
4. Security: End-to-end encryption, OAuth 2.0, RBAC, and comprehensive audit logging
5. Maintainability: Clear separation of concerns, comprehensive monitoring, and documentation
6. Extensibility: Plugin-based integration framework for external systems

The system is designed to support thousands of concurrent users while maintaining high performance and reliability standards. All architectural decisions are documented with clear rationale and consequences, enabling informed future modifications and enhancements.

Document Control

Document Classification: Internal - Technical
Last Updated: 2024-01-22
Next Review: 2024-04-22

Data Model & ERD

Comprehensive Data Model Documentation

Voice-Centric Software Development and Data Science Agent

1. Entity-Relationship Diagram

erDiagram
    USER ||--o{ PROJECT : creates
    USER ||--o{ VOICE_SESSION : initiates
    USER ||--o{ COLLABORATION : participates
    USER ||--o{ NOTIFICATION : receives
    USER ||--o{ API_KEY : owns
    
    PROJECT ||--o{ TASK : contains
    PROJECT ||--o{ CODE_ARTIFACT : produces
    PROJECT ||--o{ DATA_ANALYSIS : contains
    PROJECT ||--o{ COLLABORATION : involves
    PROJECT ||--o{ PROJECT_SETTING : configures
    
    VOICE_SESSION ||--o{ VOICE_COMMAND : records
    VOICE_SESSION ||--o{ EXECUTION_LOG : generates
    VOICE_SESSION ||--o{ SESSION_TRANSCRIPT : documents
    
    VOICE_COMMAND ||--o{ COMMAND_PARAMETER : contains
    VOICE_COMMAND ||--o{ EXECUTION_RESULT : produces
    
    TASK ||--o{ SUBTASK : decomposes
    TASK ||--o{ TASK_DEPENDENCY : relates
    TASK ||--o{ TASK_ASSIGNMENT : assigns
    
    CODE_ARTIFACT ||--o{ CODE_VERSION : versions
    CODE_ARTIFACT ||--o{ CODE_REVIEW : undergoes
    CODE_ARTIFACT ||--o{ DEPENDENCY : references
    
    DATA_ANALYSIS ||--o{ DATASET : analyzes
    DATA_ANALYSIS ||--o{ ANALYSIS_RESULT : produces
    DATA_ANALYSIS ||--o{ VISUALIZATION : generates
    
    DATASET ||--o{ DATA_QUALITY_METRIC : measures
    DATASET ||--o{ DATA_LINEAGE : tracks
    
    COLLABORATION ||--o{ COLLABORATION_MESSAGE : contains
    COLLABORATION ||--o{ SHARED_RESOURCE : shares
    
    EXECUTION_LOG ||--o{ ERROR_LOG : contains
    EXECUTION_LOG ||--o{ PERFORMANCE_METRIC : records
    
    USER {
        string user_id PK "UUID"
        string email UK "Unique email address"
        string username UK "Unique username"
        string full_name "User's full name"
        string password_hash "Bcrypt hashed password"
        string role "admin, developer, data_scientist, manager"
        string avatar_url "Profile picture URL"
        string bio "User biography"
        string preferred_language "en, es, fr, de, zh"
        string timezone "IANA timezone"
        boolean mfa_enabled "Multi-factor authentication"
        datetime created_at "Account creation timestamp"
        datetime updated_at "Last profile update"
        datetime last_login "Last login timestamp"
        boolean is_active "Account status"
        string organization_id FK "Organization reference"
    }
    
    PROJECT {
        string project_id PK "UUID"
        string user_id FK "Project owner"
        string name "Project name"
        string description "Project description"
        string type "web, mobile, data_science, ml_model, devops"
        string status "active, archived, deleted"
        string programming_language "python, javascript, java, go, rust"
        string repository_url "Git repository URL"
        string documentation_url "Project documentation link"
        datetime created_at "Project creation date"
        datetime updated_at "Last modification date"
        datetime archived_at "Archive timestamp"
        string visibility "public, private, internal"
        int estimated_duration_hours "Estimated project duration"
        string priority "low, medium, high, critical"
    }
    
    VOICE_SESSION {
        string session_id PK "UUID"
        string user_id FK "Session initiator"
        string project_id FK "Associated project"
        datetime start_time "Session start timestamp"
        datetime end_time "Session end timestamp"
        int duration_seconds "Total session duration"
        string status "active, completed, interrupted, error"
        string voice_model "whisper-large, whisper-medium, custom"
        float confidence_score "Speech recognition confidence"
        string language_detected "Detected language code"
        int command_count "Number of commands executed"
        string device_type "desktop, mobile, headset"
        string ip_address "Client IP address"
        string user_agent "Client user agent"
        boolean recording_enabled "Session recording status"
        string transcript_status "pending, processing, completed"
    }
    
    VOICE_COMMAND {
        string command_id PK "UUID"
        string session_id FK "Parent session"
        string user_id FK "Command issuer"
        string raw_text "Original voice input"
        string normalized_text "Processed command text"
        string command_type "code_generation, data_analysis, project_management, debugging"
        string intent "create, modify, delete, analyze, review"
        string entity_type "function, class, module, test, documentation"
        datetime timestamp "Command execution time"
        int execution_time_ms "Command processing duration"
        string status "pending, processing, completed, failed, cancelled"
        float nlp_confidence "NLP model confidence score"
        string error_message "Error details if failed"
        string result_summary "Brief execution result"
    }
    
    COMMAND_PARAMETER {
        string parameter_id PK "UUID"
        string command_id FK "Parent command"
        string parameter_name "Parameter identifier"
        string parameter_value "Parameter value"
        string parameter_type "string, integer, boolean, array, object"
        boolean is_required "Mandatory parameter flag"
        string validation_rule "Validation regex or rule"
        string description "Parameter description"
    }
    
    EXECUTION_RESULT {
        string result_id PK "UUID"
        string command_id FK "Associated command"
        string session_id FK "Parent session"
        string result_type "code, analysis, report, error, warning"
        string content "Result content"
        string format "json, markdown, html, plain_text"
        int size_bytes "Result size in bytes"
        string artifact_id FK "Generated artifact reference"
        datetime created_at "Result creation timestamp"
        boolean requires_review "Manual review needed flag"
        string review_status "pending, approved, rejected"
    }
    
    TASK {
        string task_id PK "UUID"
        string project_id FK "Associated project"
        string user_id FK "Task creator"
        string title "Task title"
        string description "Detailed task description"
        string status "backlog, todo, in_progress, review, completed, blocked"
        string priority "low, medium, high, critical"
        string category "feature, bug, refactor, documentation, testing"
        datetime created_at "Task creation date"
        datetime updated_at "Last update timestamp"
        datetime due_date "Task deadline"
        datetime completed_at "Completion timestamp"
        int estimated_hours "Estimated effort"
        int actual_hours "Actual effort spent"
        string assigned_to FK "Assigned user"
        int story_points "Agile story points"
    }
    
    SUBTASK {
        string subtask_id PK "UUID"
        string task_id FK "Parent task"
        string title "Subtask title"
        string description "Subtask description"
        string status "pending, in_progress, completed"
        boolean is_completed "Completion flag"
        datetime created_at "Creation timestamp"
        datetime completed_at "Completion timestamp"
        int order_index "Display order"
    }
    
    TASK_DEPENDENCY {
        string dependency_id PK "UUID"
        string task_id FK "Dependent task"
        string depends_on_task_id FK "Required task"
        string dependency_type "blocks, relates_to, duplicates, is_duplicated_by"
        datetime created_at "Dependency creation date"
    }
    
    TASK_ASSIGNMENT {
        string assignment_id PK "UUID"
        string task_id FK "Assigned task"
        string user_id FK "Assigned user"
        datetime assigned_at "Assignment timestamp"
        datetime unassigned_at "Unassignment timestamp"
        string assignment_reason "Reason for assignment"
    }
    
    CODE_ARTIFACT {
        string artifact_id PK "UUID"
        string project_id FK "Parent project"
        string user_id FK "Creator"
        string name "Artifact name"
        string file_path "Repository file path"
        string language "python, javascript, java, go, rust, sql"
        string artifact_type "function, class, module, test, script, config"
        string content "Source code content"
        int lines_of_code "LOC count"
        string status "draft, review, approved, deployed, deprecated"
        datetime created_at "Creation timestamp"
        datetime updated_at "Last modification"
        string git_commit_hash "Associated commit hash"
        string git_branch "Git branch name"
        float complexity_score "Cyclomatic complexity"
        float test_coverage "Test coverage percentage"
    }
    
    CODE_VERSION {
        string version_id PK "UUID"
        string artifact_id FK "Parent artifact"
        string version_number "Semantic version"
        string content "Version content"
        string change_summary "Change description"
        string user_id FK "Version creator"
        datetime created_at "Version creation date"
        string git_commit_hash "Associated commit"
        int lines_added "Added lines count"
        int lines_removed "Removed lines count"
    }
    
    CODE_REVIEW {
        string review_id PK "UUID"
        string artifact_id FK "Reviewed artifact"
        string reviewer_id FK "Reviewer user"
        string status "pending, approved, changes_requested, rejected"
        string comments "Review comments"
        datetime created_at "Review start date"
        datetime completed_at "Review completion date"
        int review_time_minutes "Review duration"
        int issues_found "Number of issues identified"
    }
    
    DEPENDENCY {
        string dependency_id PK "UUID"
        string artifact_id FK "Dependent artifact"
        string dependency_name "Dependency name"
        string dependency_version "Version specification"
        string dependency_type "internal, external, framework, library"
        string package_manager "npm, pip, maven, cargo, go"
        string license "License type"
        boolean is_security_risk "Security vulnerability flag"
        datetime last_checked "Last security check date"
    }
    
    DATA_ANALYSIS {
        string analysis_id PK "UUID"
        string project_id FK "Associated project"
        string user_id FK "Analysis creator"
        string title "Analysis title"
        string description "Analysis description"
        string analysis_type "exploratory, statistical, predictive, clustering, classification"
        string status "draft, in_progress, completed, archived"
        datetime created_at "Creation timestamp"
        datetime updated_at "Last update"
        datetime completed_at "Completion timestamp"
        string notebook_url "Jupyter notebook URL"
        string methodology "Analysis methodology"
        string findings_summary "Key findings summary"
    }
    
    DATASET {
        string dataset_id PK "UUID"
        string analysis_id FK "Associated analysis"
        string user_id FK "Dataset owner"
        string name "Dataset name"
        string description "Dataset description"
        string source "Data source"
        string format "csv, json, parquet, sql, excel"
        string storage_location "S3, GCS, local, database"
        int row_count "Number of rows"
        int column_count "Number of columns"
        int size_bytes "Dataset size in bytes"
        datetime created_at "Creation timestamp"
        datetime updated_at "Last update"
        datetime last_accessed "Last access timestamp"
        string access_level "public, private, internal"
        boolean is_sensitive "Sensitive data flag"
    }
    
    ANALYSIS_RESULT {
        string result_id PK "UUID"
        string analysis_id FK "Parent analysis"
        string result_type "metric, model, prediction, insight, anomaly"
        string content "Result content"
        string format "json, markdown, html, image"
        float confidence_score "Result confidence level"
        datetime created_at "Creation timestamp"
        string visualization_id FK "Associated visualization"
    }
    
    VISUALIZATION {
        string visualization_id PK "UUID"
        string analysis_id FK "Associated analysis"
        string title "Visualization title"
        string chart_type "line, bar, scatter, heatmap, pie, box, violin"
        string data_source "Data source reference"
        string image_url "Visualization image URL"
        string interactive_url "Interactive visualization URL"
        datetime created_at "Creation timestamp"
        string description "Visualization description"
    }
    
    DATA_QUALITY_METRIC {
        string metric_id PK "UUID"
        string dataset_id FK "Associated dataset"
        string metric_name "Metric name"
        float metric_value "Metric value"
        string metric_type "completeness, accuracy, consistency, timeliness"
        datetime measured_at "Measurement timestamp"
        string status "good, warning, critical"
    }
    
    DATA_LINEAGE {
        string lineage_id PK "UUID"
        string dataset_id FK "Dataset"
        string source_dataset_id FK "Source dataset"
        string transformation_description "Transformation details"
        datetime created_at "Lineage creation date"
        string transformation_code "Transformation code"
    }
    
    COLLABORATION {
        string collaboration_id PK "UUID"
        string project_id FK "Associated project"
        string initiator_id FK "Collaboration initiator"
        string collaborator_id FK "Collaborating user"
        string collaboration_type "pair_programming, code_review, data_analysis, brainstorm"
        string status "active, completed, cancelled"
        datetime started_at "Start timestamp"
        datetime ended_at "End timestamp"
        string access_level "view, comment, edit, admin"
    }
    
    COLLABORATION_MESSAGE {
        string message_id PK "UUID"
        string collaboration_id FK "Parent collaboration"
        string user_id FK "Message sender"
        string content "Message content"
        string message_type "text, code_snippet, file, link, mention"
        datetime created_at "Message timestamp"
        datetime updated_at "Last edit timestamp"
        boolean is_pinned "Pinned message flag"
    }
    
    SHARED_RESOURCE {
        string resource_id PK "UUID"
        string collaboration_id FK "Associated collaboration"
        string resource_type "code, data, document, link, artifact"
        string resource_reference "Resource identifier"
        string access_level "view, edit, admin"
        datetime shared_at "Sharing timestamp"
        datetime expires_at "Expiration timestamp"
    }
    
    EXECUTION_LOG {
        string log_id PK "UUID"
        string session_id FK "Parent session"
        string command_id FK "Associated command"
        string user_id FK "User who triggered"
        string action "execute, compile, test, deploy, analyze"
        string status "success, failure, warning, partial"
        datetime timestamp "Execution timestamp"
        int duration_ms "Execution duration"
        string output "Execution output"
        string environment "development, staging, production"
    }
    
    ERROR_LOG {
        string error_id PK "UUID"
        string log_id FK "Parent execution log"
        string error_type "syntax, runtime, logic, security, performance"
        string error_message "Error message"
        string error_code "Error code"
        string stack_trace "Stack trace"
        string file_path "File where error occurred"
        int line_number "Line number"
        datetime timestamp "Error timestamp"
        string severity "low, medium, high, critical"
    }
    
    PERFORMANCE_METRIC {
        string metric_id PK "UUID"
        string log_id FK "Associated execution log"
        string metric_name "Metric name"
        float metric_value "Metric value"
        string unit "ms, MB, %, count"
        datetime measured_at "Measurement timestamp"
        string threshold_status "normal, warning, critical"
    }
    
    SESSION_TRANSCRIPT {
        string transcript_id PK "UUID"
        string session_id FK "Parent session"
        string full_transcript "Complete session transcript"
        string summary "Session summary"
        int word_count "Total words"
        datetime created_at "Creation timestamp"
        string language "Detected language"
        boolean is_archived "Archive status"
    }
    
    NOTIFICATION {
        string notification_id PK "UUID"
        string user_id FK "Recipient user"
        string notification_type "task_assigned, code_review, collaboration_invite, error_alert, milestone"
        string title "Notification title"
        string message "Notification message"
        string related_entity_id "Related entity reference"
        datetime created_at "Creation timestamp"
        datetime read_at "Read timestamp"
        boolean is_read "Read status"
        string priority "low, medium, high"
    }
    
    API_KEY {
        string key_id PK "UUID"
        string user_id FK "Key owner"
        string key_hash "Hashed API key"
        string key_name "Key name/description"
        string[] scopes "Authorized scopes"
        datetime created_at "Creation timestamp"
        datetime last_used "Last usage timestamp"
        datetime expires_at "Expiration timestamp"
        boolean is_active "Active status"
        int rate_limit "Requests per minute"
    }
    
    PROJECT_SETTING {
        string setting_id PK "UUID"
        string project_id FK "Associated project"
        string setting_key "Setting identifier"
        string setting_value "Setting value"
        string setting_type "string, integer, boolean, json"
        string description "Setting description"
        datetime updated_at "Last update timestamp"
    }

2. Entity Descriptions

2.1 USER Entity

Purpose & Business Meaning: Represents individual users of the voice-centric development platform. Stores authentication credentials, profile information, preferences, and account status. Central to access control and personalization.

Attributes:

Relationships:

• 1:N with PROJECT - User creates and owns projects
• 1:N with VOICE_SESSION - User initiates voice sessions
• 1:N with COLLABORATION - User participates in collaborations
• 1:N with NOTIFICATION - User receives notifications
• 1:N with API_KEY - User owns API keys

Indexes & Performance:

• Primary Index: user_id (clustered)
• Unique Indexes: email, username
• Search Index: full_name (for user search)
• Foreign Key Index: organization_id
• Composite Index: (is_active, created_at) for active user queries

Constraints:

• Email must be valid RFC 5322 format
• Username: 3-50 alphanumeric characters, underscores allowed
• Password hash must be 60 characters (Bcrypt)
• Timezone must be valid IANA identifier

2.2 PROJECT Entity

Purpose & Business Meaning: Represents development or data science projects. Serves as the primary organizational unit for tasks, code artifacts, analyses, and collaborations. Tracks project metadata, status, and configuration.

Attributes:

Relationships:

• N:1 with USER - Owned by a user
• 1:N with TASK - Contains multiple tasks
• 1:N with CODE_ARTIFACT - Produces code artifacts
• 1:N with DATA_ANALYSIS - Contains data analyses
• 1:N with COLLABORATION - Involves collaborations
• 1:N with PROJECT_SETTING - Has configuration settings

Indexes & Performance:

• Primary Index: project_id (clustered)
• Foreign Key Index: user_id
• Composite Index: (user_id, status, created_at) for user project listing
• Search Index: name (full-text search)
• Status Index: (status, archived_at) for filtering

Constraints:

• Name length: 1-255 characters
• Repository URL must be valid Git URL format
• Estimated duration must be positive integer
• Status transitions: active → archived → deleted (one-way)

2.3 VOICE_SESSION Entity

Purpose & Business Meaning: Represents individual voice interaction sessions. Tracks voice input, processing metadata, command execution, and session lifecycle. Critical for audit trails and performance monitoring.

Attributes:

Relationships:

• N:1 with USER - Initiated by user
• N:1 with PROJECT - Associated with project
• 1:N with VOICE_COMMAND - Records multiple commands
• 1:N with EXECUTION_LOG - Generates execution logs
• 1:1 with SESSION_TRANSCRIPT - Has transcript

Indexes & Performance:

• Primary Index: session_id (clustered)
• Foreign Key Indexes: user_id, project_id
• Composite Index: (user_id, start_time DESC) for session history
• Status Index: (status, end_time) for active session queries
• Time-based Index: (start_time) for time-range queries

Constraints:

• Duration must be non-negative
• Confidence score: 0.0 to 1.0
• Language code must be valid ISO 639-1
• IP address must be valid IPv4 or IPv6
• End time must be >= start time

2.4 VOICE_COMMAND Entity

Purpose & Business Meaning: Represents individual voice commands within a session. Stores raw and processed command text, intent classification, execution status, and results. Essential for command tracking and NLP model training.

Attributes:

Relationships:

• N:1 with VOICE_SESSION - Belongs to session
• N:1 with USER - Issued by user
• 1:N with COMMAND_PARAMETER - Contains parameters
• 1:N with EXECUTION_RESULT - Produces results

Indexes & Performance:

• Primary Index: command_id (clustered)
• Foreign Key Indexes: session_id, user_id
• Composite Index: (session_id, timestamp) for session command retrieval
• Status Index: (status, timestamp) for pending command queries
• Intent Index: (command_type, intent) for command analytics

Constraints:

• Raw text length: 1-5000 characters
• Normalized text length: 1-5000 characters
• Execution time must be non-negative
• NLP confidence: 0.0 to 1.0

• Status transitions: pending → processing → (completed

  failed

  cancelled)

2.5 TASK Entity

Purpose & Business Meaning: Represents work items within projects. Supports agile project management with status tracking, assignments, dependencies, and effort estimation. Integrates with voice commands for task creation and updates.

Attributes:

Relationships:

• N:1 with PROJECT - Belongs to project
• N:1 with USER - Created by user
• 1:N with SUBTASK - Decomposes into subtasks
• 1:N with TASK_DEPENDENCY - Has dependencies
• 1:N with TASK_ASSIGNMENT - Has assignments

Indexes & Performance:

• Primary Index: task_id (clustered)
• Foreign Key Indexes: project_id, user_id, assigned_to
• Composite Index: (project_id, status, priority) for task filtering
• Status Index: (status, due_date) for task board queries
• Assignment Index: (assigned_to, status) for user task lists

Constraints:

• Title length: 1-255 characters
• Estimated hours must be positive
• Actual hours must be non-negative
• Due date must be in future
• Completed date must be >= created date
• Story points must be positive

2.6 CODE_ARTIFACT Entity

Purpose & Business Meaning: Represents code components generated or managed through voice commands. Tracks source code, metadata, quality metrics, and version history. Integrates with Git repositories and code review workflows.

Attributes:

Relationships:

• N:1 with PROJECT - Belongs to project
• N:1 with USER - Created by user
• 1:N with CODE_VERSION - Has version history
• 1:N with CODE_REVIEW - Undergoes reviews
• 1:N with DEPENDENCY - References dependencies

Indexes & Performance:

• Primary Index: artifact_id (clustered)
• Foreign Key Indexes: project_id, user_id
• Composite Index: (project_id, artifact_type, status) for artifact filtering
• Search Index: name (full-text search)
• Status Index: (status, updated_at) for recent artifacts

Constraints:

• Name length: 1-255 characters
• File path must be valid relative path
• Language must be supported language
• LOC must be positive
• Complexity score must be non-negative
• Test coverage: 0-100%

• Status transitions: draft → review → (approved

  rejected) → deployed

2.7 DATA_ANALYSIS Entity

Purpose & Business Meaning: Represents data science analyses and experiments. Tracks analysis methodology, findings, and associated datasets. Integrates with Jupyter notebooks and visualization tools.

Attributes:

Relationships:

• N:1 with PROJECT - Belongs to project
• N:1 with USER - Created by user
• 1:N with DATASET - Analyzes datasets
• 1:N with ANALYSIS_RESULT - Produces results
• 1:N with VISUALIZATION - Generates visualizations

Indexes & Performance:

• Primary Index: analysis_id (clustered)
• Foreign Key Indexes: project_id, user_id
• Composite Index: (project_id, status, created_at) for analysis listing
• Status Index: (status, completed_at) for completed analysis queries
• Search Index: title (full-text search)

Constraints:

• Title length: 1-255 characters
• Analysis type must be valid type
• Completed date must be >= created date
• Status transitions: draft → in_progress → completed → archived

2.8 DATASET Entity

Purpose & Business Meaning: Represents data sources used in analyses. Tracks data location, format, quality metrics, and access controls. Supports data lineage tracking and quality monitoring.

Attributes:

Relationships:

• N:1 with ANALYSIS - Used in analysis
• N:1 with USER - Owned by user
• 1:N with DATA_QUALITY_METRIC - Has quality metrics
• 1:N with DATA_LINEAGE - Has lineage tracking

Indexes & Performance:

• Primary Index: dataset_id (clustered)
• Foreign Key Indexes: analysis_id, user_id
• Composite Index: (user_id, access_level, created_at) for user dataset listing
• Search Index: name (full-text search)
• Access Index: (access_level, is_sensitive) for access control

Constraints:

• Name length: 1-255 characters
• Row count must be non-negative
• Column count must be positive
• Size must be positive
• Format must be supported format
• Storage location must be valid

2.9 EXECUTION_LOG Entity

Purpose & Business Meaning: Records execution history of commands and operations. Tracks success/failure status, performance metrics, and output. Essential for debugging, auditing, and performance analysis.

Attributes:

Relationships:

• N:1 with VOICE_SESSION - Belongs to session
• N:1 with VOICE_COMMAND - Associated with command
• N:1 with USER - Triggered by user
• 1:N with ERROR_LOG - Contains error logs
• 1:N with PERFORMANCE_METRIC - Records metrics

Indexes & Performance:

• Primary Index: log_id (clustered)
• Foreign Key Indexes: session_id, command_id, user_id
• Composite Index: (session_id, timestamp DESC) for session log retrieval
• Status Index: (status, timestamp) for failure analysis
• Environment Index: (environment, timestamp) for environment-specific queries
• Time-based Index: (timestamp) for time-range queries with TTL

Constraints:

• Duration must be non-negative
• Status must be valid status
• Action must be valid action
• Environment must be valid environment
• Output length: 0-1MB

2.10 COLLABORATION Entity

Purpose & Business Meaning: Represents collaborative sessions between users. Tracks pair programming, code reviews, and data analysis collaborations. Manages access levels and collaboration lifecycle.

Attributes:

Relationships:

• N:1 with PROJECT - Associated with project
• N:1 with USER (initiator) - Initiated by user
• N:1 with USER (collaborator) - Involves collaborator
• 1:N with COLLABORATION_MESSAGE - Contains messages
• 1:N with SHARED_RESOURCE - Shares resources

Indexes & Performance:

• Primary Index: collaboration_id (clustered)
• Foreign Key Indexes: project_id, initiator_id, collaborator_id
• Composite Index: (project_id, status, started_at) for active collaborations
• User Index: (initiator_id, status) for user collaborations
• Status Index: (status, ended_at) for completed collaboration queries

Constraints:

• Initiator and collaborator must be different users
• Access level must be valid level
• Ended date must be >= started date

• Status transitions: active → (completed

  cancelled)

3. Comprehensive Data Dictionary

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# Complete Data Dictionary

## USER Table
| Attribute | Type | Size | Nullable | Default | Constraints | Description |
|-----------|------|------|----------|---------|-------------|-------------|
| user_id | UUID | 16 | NO | uuid_generate_v4() | PK | Unique user identifier |
| email | VARCHAR | 255 | NO | NULL | UK, NOT NULL | Email address for login |
| username | VARCHAR | 100 | NO | NULL | UK, NOT NULL | Display username |
| full_name | VARCHAR | 255 | NO | NULL | NOT NULL | User's full name |
| password_hash | VARCHAR | 255 | NO | NULL | NOT NULL | Bcrypt hash (60 chars) |
| role | ENUM | - | NO | 'developer' | CHECK IN (admin, developer, data_scientist, manager) | User role |
| avatar_url | VARCHAR | 500 | YES | NULL | URL format | Profile picture URL |
| bio | TEXT | 500 | YES | NULL | MAX 500 chars | User biography |
| preferred_language | VARCHAR | 10 | NO | 'en' | ISO 639-1 code | Language preference |
| timezone | VARCHAR | 50 | NO | 'UTC' | IANA timezone | User timezone |
| mfa_enabled | BOOLEAN | 1 | NO | false | - | MFA status |
| created_at | TIMESTAMP | 8 | NO | NOW() | - | Account creation |
| updated_at | TIMESTAMP | 8 | NO | NOW() | - | Last update |
| last_login | TIMESTAMP | 8 | YES | NULL | - | Last login time |
| is_active | BOOLEAN | 1 | NO | true | - | Account status |
| organization_id | UUID | 16 | YES | NULL | FK → ORGANIZATION | Organization reference |

## PROJECT Table
| Attribute | Type | Size | Nullable | Default | Constraints | Description |
|-----------|------|------|----------|---------|-------------|-------------|
| project_id | UUID | 16 | NO | uuid_generate_v4() | PK | Unique project ID |
| user_id | UUID | 16 | NO | NULL | FK → USER | Project owner |
| name | VARCHAR | 255 | NO | NULL | UK(user_id), NOT NULL | Project name |
| description | TEXT | 2000 | YES | NULL | - | Project description |
| type | ENUM | - | NO | NULL | CHECK IN (web, mobile, data_science, ml_model, devops) | Project type |
| status | ENUM | - | NO | 'active' | CHECK IN (active, archived, deleted) | Project status |
| programming_language | VARCHAR | 50 | YES | NULL | - | Primary language |
| repository_url | VARCHAR | 500 | YES | NULL | URL format | Git repository URL |
| documentation_url | VARCHAR | 500 | YES | NULL | URL format | Documentation link |
| created_at | TIMESTAMP | 8 | NO | NOW() | - | Creation date |
| updated_at | TIMESTAMP | 8 | NO | NOW() | - | Last update |
| archived_at | TIMESTAMP | 8 | YES | NULL | - | Archive timestamp |
| visibility | ENUM | - | NO | 'private' | CHECK IN (public, private, internal) | Visibility level |
| estimated_duration_hours | INT | 4 | YES | NULL | CHECK > 0 | Estimated duration |
| priority | ENUM | - | NO | 'medium' | CHECK IN (low, medium, high, critical) | Project priority |

## VOICE_SESSION Table
| Attribute | Type | Size | Nullable | Default | Constraints | Description |
|-----------|------|------|----------|---------|-------------|-------------|
| session_id | UUID | 16 | NO | uuid_generate_v4() | PK | Unique session ID |
| user_id | UUID | 16 | NO | NULL | FK → USER | Session initiator |
| project_id | UUID | 16 | YES | NULL | FK → PROJECT | Associated project |
| start_time | TIMESTAMP | 8 | NO | NOW() | - | Session start |
| end_time | TIMESTAMP | 8 | YES | NULL | - | Session end |
| duration_seconds | INT | 4 | YES | NULL | CHECK >= 0 | Total duration |
| status | ENUM | - | NO | 'active' | CHECK IN (active, completed, interrupted, error) | Session status |
| voice_model | VARCHAR | 50 | NO | 'whisper-large' | - | Speech model |
| confidence_score | FLOAT | 4 | YES | NULL | CHECK 0-1 | Recognition confidence |
| language_detected | VARCHAR | 10 | NO | NULL | ISO 639-1 code | Detected language |
| command_count | INT | 4 | NO | 0 | CHECK >= 0 | Command count |
| device_type | VARCHAR | 50 | NO | NULL | - | Device type |
| ip_address | VARCHAR | 45 | NO | NULL | IPv4/IPv6 format | Client IP |
| user_agent | VARCHAR | 500 | NO | NULL | - | User agent string |
| recording_enabled | BOOLEAN | 1 | NO | false | - | Recording status |
| transcript_status | ENUM | - | NO | 'pending' | CHECK IN (pending, processing, completed) | Transcript status |

## VOICE_COMMAND Table
| Attribute | Type | Size | Nullable | Default | Constraints | Description |
|-----------|------|------|----------|---------|-------------|-------------|
| command_id | UUID | 16 | NO | uuid_generate_v4() | PK | Unique command ID |
| session_id | UUID | 16 | NO | NULL | FK → VOICE_SESSION | Parent session |
| user_id | UUID | 16 | NO | NULL | FK → USER | Command issuer |
| raw_text | TEXT | 5000 | NO | NULL | NOT NULL | Original input |
| normalized_text | TEXT | 5000 | NO | NULL | NOT NULL | Processed text |
| command_type | ENUM | - | NO | NULL | CHECK IN (code_generation, data_analysis, project_management, debugging) | Command type |
| intent | ENUM | - | NO | NULL | CHECK IN (create, modify, delete, analyze, review) | Intent |
| entity_type | VARCHAR | 50 | YES | NULL | - | Entity type |
| timestamp | TIMESTAMP | 8 | NO | NOW() | - | Execution time |
| execution_time_ms | INT | 4 | YES | NULL | CHECK >= 0 | Processing duration |
| status | ENUM | - | NO | 'pending' | CHECK IN (pending, processing, completed, failed, cancelled) | Execution status |
| nlp_confidence | FLOAT | 4 | YES | NULL | CHECK 0-1 | NLP confidence |
| error_message | TEXT | 1000 | YES | NULL | - | Error details |
| result_summary | TEXT | 500 | YES | NULL | - | Result summary |

## TASK Table
| Attribute | Type | Size | Nullable | Default | Constraints | Description |
|-----------|------|------|----------|---------|-------------|-------------|
| task_id | UUID | 16 | NO | uuid_generate_v4() | PK | Unique task ID |
| project_id | UUID | 16 | NO | NULL | FK → PROJECT | Associated project |
| user_id | UUID | 16 | NO | NULL | FK → USER | Task creator |
| title | VARCHAR | 255 | NO | NULL | NOT NULL | Task title |
| description | TEXT | 2000 | YES | NULL | - | Task description |
| status | ENUM | - | NO | 'backlog' | CHECK IN (backlog, todo, in_progress, review, completed, blocked) | Task status |
| priority | ENUM | - | NO | 'medium' | CHECK IN (low, medium, high, critical) | Task priority |
| category | ENUM | - | NO | NULL | CHECK IN (feature, bug, refactor, documentation, testing) | Task category |
| created_at | TIMESTAMP | 8 | NO | NOW() | - | Creation date |
| updated_at | TIMESTAMP | 8 | NO | NOW() | - | Last update |
| due_date | DATE | 3 | YES | NULL | - | Task deadline |
| completed_at | TIMESTAMP | 8 | YES | NULL | - | Completion time |
| estimated_hours | INT | 4 | YES | NULL | CHECK > 0 | Estimated effort |
| actual_hours | INT | 4 | YES | NULL | CHECK >= 0 | Actual effort |
| assigned_to | UUID | 16 | YES | NULL | FK → USER | Assigned user |
| story_points | INT | 4 | YES | NULL | CHECK > 0 | Story points |

## CODE_ARTIFACT Table
| Attribute | Type | Size | Nullable | Default | Constraints | Description |
|-----------|------|------|----------|---------|-------------|-------------|
| artifact_id | UUID | 16 | NO | uuid_generate_v4() | PK | Unique artifact ID |
| project_id | UUID | 16 | NO | NULL | FK → PROJECT | Parent project |
| user_id | UUID | 16 | NO | NULL | FK → USER | Creator |
| name | VARCHAR | 255 | NO | NULL | NOT NULL | Artifact name |
| file_path | VARCHAR | 500 | NO | NULL | NOT NULL | File path |
| language | VARCHAR | 50 | NO | NULL | NOT NULL | Programming language |
| artifact_type | ENUM | - | NO | NULL | CHECK IN (function, class, module, test, script, config) | Artifact type |
| content | LONGTEXT | 16MB | NO | NULL | NOT NULL | Source code |
| lines_of_code | INT | 4 | YES | NULL | CHECK > 0 | LOC count |
| status | ENUM | - | NO | 'draft' | CHECK IN (draft, review, approved, deployed, deprecated) | Status |
| created_at | TIMESTAMP | 8 | NO | NOW() | - | Creation date |
| updated_at | TIMESTAMP | 8 | NO | NOW() | - | Last update |
| git_commit_hash | VARCHAR | 40 | YES | NULL | SHA-1 format | Commit hash |
| git_branch | VARCHAR | 255 | YES | NULL | - | Git branch |
| complexity_score | FLOAT | 4 | YES | NULL | CHECK >= 0 | Cyclomatic complexity |
| test_coverage | FLOAT | 4 | YES | NULL | CHECK 0-100 | Test coverage % |

## DATA_ANALYSIS Table
| Attribute | Type | Size | Nullable | Default | Constraints | Description |
|-----------|------|------|----------|---------|-------------|-------------|
| analysis_id | UUID | 16 | NO | uuid_generate_v4() | PK | Unique analysis ID |
| project_id | UUID | 16 | NO | NULL | FK → PROJECT | Associated project |
| user_id | UUID | 16 | NO | NULL | FK → USER | Analysis creator |
| title | VARCHAR | 255 | NO | NULL | NOT NULL | Analysis title |
| description | TEXT | 2000 | YES | NULL | - | Analysis description |
| analysis_type | ENUM | - | NO | NULL | CHECK IN (exploratory, statistical, predictive, clustering, classification) | Analysis type |
| status | ENUM | - | NO | 'draft' | CHECK IN (draft, in_progress, completed, archived) | Status |
| created_at | TIMESTAMP | 8 | NO | NOW() | - | Creation date |
| updated_at | TIMESTAMP | 8 | NO | NOW() | - | Last update |
| completed_at | TIMESTAMP | 8 | YES | NULL | - | Completion date |
| notebook_url | VARCHAR | 500 | YES | NULL | URL format | Notebook URL |
| methodology | TEXT | 2000 | YES | NULL | - | Methodology |
| findings_summary | TEXT | 2000 | YES | NULL | - | Findings summary |

## DATASET Table
| Attribute | Type | Size | Nullable | Default | Constraints | Description |
|-----------|------|------|----------|---------|-------------|-------------|
| dataset_id | UUID | 16 | NO | uuid_generate_v4() | PK | Unique dataset ID |
| analysis_id | UUID | 16 | YES | NULL | FK → DATA_ANALYSIS | Associated analysis |
| user_id | UUID | 16 | NO | NULL | FK → USER | Dataset owner |
| name | VARCHAR | 255 | NO | NULL | NOT NULL | Dataset name |
| description | TEXT | 2000 | YES | NULL | - | Dataset description |
| source | VARCHAR | 500 | NO | NULL | NOT NULL | Data source |
| format | ENUM | - | NO | NULL | CHECK IN (csv, json, parquet, sql, excel) | Data format |
| storage_location | VARCHAR | 500 | NO | NULL | NOT NULL | Storage location |
| row_count | INT | 4 | YES | NULL | CHECK >= 0 | Row count |
| column_count | INT | 4 | NO | NULL | CHECK > 0 | Column count |
| size_bytes | BIGINT | 8 | NO | NULL | CHECK > 0 | Size in bytes |
| created_at | TIMESTAMP | 8 | NO | NOW() | - | Creation date |
| updated_at | TIMESTAMP | 8 | NO | NOW() | - | Last update |
| last_accessed | TIMESTAMP | 8 | YES | NULL | - | Last access |
| access_level | ENUM | - | NO | 'private' | CHECK IN (public, private, internal) | Access level |
| is_sensitive | BOOLEAN | 1 | NO | false | - | Sensitive flag |

## EXECUTION_LOG Table
| Attribute | Type | Size | Nullable | Default | Constraints | Description |
|-----------|------|------|----------|---------|-------------|-------------|
| log_id | UUID | 16 | NO | uuid_generate_v4() | PK | Unique log ID |
| session_id | UUID | 16 | NO | NULL | FK → VOICE_SESSION | Parent session |
| command_id | UUID | 16 | YES | NULL | FK → VOICE_COMMAND | Associated command |
| user_id | UUID | 16 | NO | NULL | FK → USER | User who triggered |
| action | ENUM | - | NO | NULL | CHECK IN (execute, compile, test, deploy, analyze) | Action type |
| status | ENUM | - | NO | NULL | CHECK IN (success, failure, warning, partial) | Execution status |
| timestamp | TIMESTAMP | 8 | NO | NOW() | - | Execution time |
| duration_ms | INT | 4 | YES | NULL | CHECK >= 0 | Duration in ms |
| output | LONGTEXT | 1MB | YES | NULL | - | Execution output |
| environment | ENUM | - | NO | NULL | CHECK IN (development, staging, production) | Environment |

## COLLABORATION Table
| Attribute | Type | Size | Nullable | Default | Constraints | Description |
|-----------|------|------|----------|---------|-------------|-------------|
| collaboration_id | UUID | 16 | NO | uuid_generate_v4() | PK | Unique collaboration ID |
| project_id | UUID | 16 | NO | NULL | FK → PROJECT | Associated project |
| initiator_id | UUID | 16 | NO | NULL | FK → USER | Collaboration initiator |
| collaborator_id | UUID | 16 | NO | NULL | FK → USER | Collaborating user |
| collaboration_type | ENUM | - | NO | NULL | CHECK IN (pair_programming, code_review, data_analysis, brainstorm) | Collaboration type |
| status | ENUM | - | NO | 'active' | CHECK IN (active, completed, cancelled) | Status |
| started_at | TIMESTAMP | 8 | NO | NOW() | - | Start time |
| ended_at | TIMESTAMP | 8 | YES | NULL | - | End time |
| access_level | ENUM | - | NO | NULL | CHECK IN (view, comment, edit, admin) | Access level |

4. Data Flow Diagram

graph TD
    A[User] -->|Voice Input| B[Voice Processing Engine]
    B -->|Audio Stream| C[Speech Recognition<br/>Whisper Model]
    C -->|Transcribed Text| D[NLP Processing<br/>Intent Classification]
    D -->|Parsed Command| E{Command Type}
    
    E -->|Code Generation| F[Code Generation<br/>Engine]
    E -->|Data Analysis| G[Data Analysis<br/>Engine]
    E -->|Project Management| H[Task Management<br/>Service]
    E -->|Debugging| I[Debug Service]
    
    F -->|Generated Code| J[Code Artifact<br/>Storage]
    G -->|Analysis Results| K[Analysis Result<br/>Storage]
    H -->|Task Updates| L[Task<br/>Database]
    I -->|Debug Info| M[Execution Log<br/>Storage]
    
    J -->|Store| N[(Primary<br/>Database)]
    K -->|Store| N
    L -->|Store| N
    M -->|Store| N
    
    N -->|Query| O[Voice Session<br/>Manager]
    O -->|Session Data| P[Session<br/>Storage]
    
    P -->|Retrieve| Q[Response<br/>Generator]
    Q -->|Formatted Response| R[Text-to-Speech<br/>Engine]
    R -->|Audio Output| A
    
    N -->|Audit Trail| S[Audit Log<br/>Service]
    S -->|Store| T[(Audit<br/>Database)]
    
    N -->|Metrics| U[Analytics<br/>Service]
    U -->|Store| V[(Analytics<br/>Database)]
    
    W[Git Repository] -->|Code Sync| J
    X[Jupyter Notebooks] -->|Analysis Sync| K
    Y[Project Management Tool] -->|Task Sync| L
    
    Z[Collaboration Service] -->|Real-time Updates| N
    AA[Notification Service] -->|Alerts| A

5. Data Validation Rules

5.1 Entity-Level Validation Rules

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## USER Entity Validation Rules

### VR-USER-001: Email Validation
- **Rule**: Email must be valid RFC 5322 format
- **Implementation**: Regex pattern: `^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$`
- **Error Message**: "Invalid email format"
- **Severity**: CRITICAL

### VR-USER-002: Username Uniqueness
- **Rule**: Username must be unique across system
- **Implementation**: Unique constraint on username column
- **Error Message**: "Username already exists"
- **Severity**: CRITICAL

### VR-USER-003: Password Strength
- **Rule**: Password must meet complexity requirements
- **Requirements**:
  - Minimum 12 characters
  - At least 1 uppercase letter
  - At least 1 lowercase letter
  - At least 1 digit
  - At least 1 special character (!@#$%^&*)
- **Implementation**: Validation function before hashing
- **Error Message**: "Password does not meet complexity requirements"
- **Severity**: CRITICAL

### VR-USER-004: Timezone Validation
- **Rule**: Timezone must be valid IANA identifier
- **Implementation**: Validate against IANA timezone database
- **Error Message**: "Invalid timezone identifier"
- **Severity**: MEDIUM

### VR-USER-005: Language Code Validation
- **Rule**: Language code must be valid ISO 639-1 code
- **Implementation**: Validate against ISO 639-1 standard
- **Error Message**: "Invalid language code"
- **Severity**: MEDIUM

---

## PROJECT Entity Validation Rules

### VR-PROJECT-001: Project Name Uniqueness
- **Rule**: Project name must be unique per user
- **Implementation**: Unique constraint on (user_id, name)
- **Error Message**: "Project name already exists for this user"
- **Severity**: CRITICAL

### VR-PROJECT-002: Repository URL Format
- **Rule**: Repository URL must be valid Git URL
- **Implementation**: Regex pattern for Git URLs
- **Pattern**: `^(https?://|git@)[a-zA-Z0-9.-]+(/|:)[a-zA-Z0-9._-]+(/[a-zA-Z0-9._-]+)?(.git)?$`
- **Error Message**: "Invalid Git repository URL"
- **Severity**: HIGH

### VR-PROJECT-003: Estimated Duration Validation
- **Rule**: Estimated duration must be positive integer
- **Implementation**: CHECK constraint: estimated_duration_hours > 0
- **Error Message**: "Estimated duration must be greater than 0"
- **Severity**: MEDIUM

### VR-PROJECT-004: Status Transition Validation
- **Rule**: Project status can only transition in specific ways
- **Valid Transitions**:
  - active → archived
  - archived → deleted
  - active → deleted (direct)
- **Implementation**: Trigger-based validation
- **Error Message**: "Invalid status transition"
- **Severity**: HIGH

### VR-PROJECT-005: Programming Language Support
- **Rule**: Programming language must be supported
- **Supported Languages**: python, javascript, java, go, rust, c++, c#, php, ruby, kotlin
- **Implementation**: CHECK constraint with ENUM
- **Error Message**: "Unsupported programming language"
- **Severity**: MEDIUM

---

## VOICE_SESSION Entity Validation Rules

### VR-SESSION-001: Session Duration Validation
- **Rule**: Session duration must be non-negative
- **Implementation**: CHECK constraint: duration_seconds >= 0
- **Error Message**: "Session duration cannot be negative"
- **Severity**: HIGH

### VR-SESSION-002: Confidence Score Range
- **Rule**: Confidence score must be between 0 and 1
- **Implementation**: CHECK constraint: confidence_score BETWEEN 0 AND 1
- **Error Message**: "Confidence score must be between 0 and 1"
- **Severity**: HIGH

### VR-SESSION-003: Language Code Validation
- **Rule**: Detected language must be valid ISO 639-1 code
- **Implementation**: Validate against ISO 639-1 standard
- **Error Message**: "Invalid language code"
- **Severity**: MEDIUM

### VR-SESSION-004: IP Address Validation
- **Rule**: IP address must be valid IPv4 or IPv6
- **Implementation**: Regex pattern for IPv4/IPv6
- **Error Message**: "Invalid IP address format"
- **Severity**: MEDIUM

### VR-SESSION-005: End Time Validation
- **Rule**: End time must be >= start time
- **Implementation**: CHECK constraint: end_time >= start_time
- **Error Message**: "End time cannot be before start time"
- **Severity**: HIGH

### VR-SESSION-006: Command Count Validation
- **Rule**: Command count must be non-negative
- **Implementation**: CHECK constraint: command_count >= 0
- **Error Message**: "Command count cannot be negative"
- **Severity**: MEDIUM

---

## VOICE_COMMAND Entity Validation Rules

### VR-COMMAND-001: Text Length Validation
- **Rule**: Raw and normalized text must be between 1 and 5000 characters
- **Implementation**: CHECK constraint: LENGTH(raw_text) BETWEEN 1 AND 5000
- **Error Message**: "Command text must be between 1 and 5000 characters"
- **Severity**: HIGH

### VR-COMMAND-002: Execution Time Validation
- **Rule**: Execution time must be non-negative
- **Implementation**: CHECK constraint: execution_time_ms >= 0
- **Error Message**: "Execution time cannot be negative"
- **Severity**: MEDIUM

### VR-COMMAND-003: NLP Confidence Validation
- **Rule**: NLP confidence must be between 0 and 1
- **Implementation**: CHECK constraint: nlp_confidence BETWEEN 0 AND 1
- **Error Message**: "NLP confidence must be between 0 and 1"
- **Severity**: MEDIUM

### VR-COMMAND-004: Command Type Validation
- **Rule**: Command type must be from predefined list
- **Valid Types**: code_generation, data_analysis, project_management, debugging
- **Implementation**: ENUM constraint
- **Error Message**: "Invalid command type"
- **Severity**: HIGH

### VR-COMMAND-005: Intent Validation
- **Rule**: Intent must be from predefined list
- **Valid Intents**: create, modify, delete, analyze, review
- **Implementation**: ENUM constraint
- **Error Message**: "Invalid intent"
- **Severity**: HIGH

### VR-COMMAND-006: Status Transition Validation
- **Rule**: Command status can only transition in specific ways
- **Valid Transitions**:
  - pending → processing → (completed|failed|cancelled)
  - pending → cancelled
- **Implementation**: Trigger-based validation
- **Error Message**: "Invalid status transition"
- **Severity**: HIGH

---

## TASK Entity Validation Rules

### VR-TASK-001: Title Length Validation
- **Rule**: Task title must be between 1 and 255 characters
- **Implementation**: CHECK constraint: LENGTH(title) BETWEEN 1 AND 255
- **Error Message**: "Task title must be between 1 and 255 characters"
- **Severity**: HIGH

### VR-TASK-002: Estimated Hours Validation
- **Rule**: Estimated hours must be positive integer
- **Implementation**: CHECK constraint: estimated_hours > 0
- **Error Message**: "Estimated hours must be greater than 0"
- **Severity**: MEDIUM

### VR-TASK-003: Actual Hours Validation
- **Rule**: Actual hours must be non-negative
- **Implementation**: CHECK constraint: actual_hours >= 0
- **Error Message**: "Actual hours cannot be negative"
- **Severity**: MEDIUM

### VR-TASK-004: Due Date Validation
- **Rule**: Due date must be in future
- **Implementation**: CHECK constraint: due_date > CURRENT_DATE
- **Error Message**: "Due date must be in the future"
- **Severity**: MEDIUM

### VR-TASK-005: Completion Date Validation
- **Rule**: Completion date must be >= creation date
- **Implementation**: CHECK constraint: completed_at >= created_at
- **Error Message**: "Completion date cannot be before creation date"
- **Severity**: HIGH

### VR-TASK-006: Story Points Validation
- **Rule**: Story points must be positive integer
- **Implementation**: CHECK constraint: story_points > 0
- **Error Message**: "Story points must be greater than 0"
- **Severity**: MEDIUM

### VR-TASK-007: Status Transition Validation
- **Rule**: Task status can only transition in specific ways
- **Valid Transitions**:
  - backlog → todo → in_progress → review → completed
  - Any status → blocked
  - blocked → (previous status)
- **Implementation**: Trigger-based validation
- **Error Message**: "Invalid status transition"
- **Severity**: HIGH

---

## CODE_ARTIFACT Entity Validation Rules

### VR-ARTIFACT-001: Name Length Validation
- **Rule**: Artifact name must be between 1 and 255 characters
- **Implementation**: CHECK constraint: LENGTH(name) BETWEEN 1 AND 255
- **Error Message**: "Artifact name must be between 1 and 255 characters"
- **Severity**: HIGH

### VR-ARTIFACT-002: File Path Validation
- **Rule**: File path must be valid relative path
- **Implementation**: Regex pattern for valid paths
- **Pattern**: `^[a-zA-Z0-9._/-]+$`
- **Error Message**: "Invalid file path format"
- **Severity**: HIGH

### VR-ARTIFACT-003: Language Support Validation
- **Rule**: Programming language must be supported
- **Supported Languages**: python, javascript, java, go, rust, c++, c#, php, ruby, kotlin, sql
- **Implementation**: ENUM constraint
- **Error Message**: "Unsupported programming language"
- **Severity**: HIGH

### VR-ARTIFACT-004: LOC Validation
- **Rule**: Lines of code must be positive integer
- **Implementation**: CHECK constraint: lines_of_code > 0
- **Error Message**: "Lines of code must be greater than 0"
- **Severity**: MEDIUM

### VR-ARTIFACT-005: Complexity Score Validation
- **Rule**: Complexity score must be non-negative
- **Implementation**: CHECK constraint: complexity_score >= 0
- **Error Message**: "Complexity score cannot be negative"
- **Severity**: MEDIUM

### VR-ARTIFACT-006: Test Coverage Validation
- **Rule**: Test coverage must be between 0 and 100
- **Implementation**: CHECK constraint: test_coverage BETWEEN 0 AND 100
- **Error Message**: "Test coverage must be between 0 and 100"
- **Severity**: MEDIUM

### VR-ARTIFACT-007: Git Commit Hash Validation
- **Rule**: Git commit hash must be valid SHA-1 hash
- **Implementation**: Regex pattern: `^[a-f0-9]{40}$`
- **Error Message**: "Invalid Git commit hash format"
- **Severity**: MEDIUM

### VR-ARTIFACT-008: Status Transition Validation
- **Rule**: Artifact status can only transition in specific ways
- **Valid Transitions**:
  - draft → review → (approved|rejected)
  - approved → deployed
  - deployed → deprecated
- **Implementation**: Trigger-based validation
- **Error Message**: "Invalid status transition"
- **Severity**: HIGH

---

## DATASET Entity Validation Rules

### VR-DATASET-001: Name Length Validation
- **Rule**: Dataset name must be between 1 and 255 characters
- **Implementation**: CHECK constraint: LENGTH(name) BETWEEN 1 AND 255
- **Error Message**: "Dataset name must be between 1 and 255 characters"
- **Severity**: HIGH

### VR-DATASET-002: Row Count Validation
- **Rule**: Row count must be non-negative
- **Implementation**: CHECK constraint: row_count >= 0
- **Error Message**: "Row count cannot be negative"
- **Severity**: MEDIUM

### VR-DATASET-003: Column Count Validation
- **Rule**: Column count must be positive
- **Implementation**: CHECK constraint: column_count > 0
- **Error Message**: "Column count must be greater than 0"
- **Severity**: HIGH

### VR-DATASET-004: Size Validation
- **Rule**: Dataset size must be positive
- **Implementation**: CHECK constraint: size_bytes > 0
- **Error Message**: "Dataset size must be greater than 0"
- **Severity**: HIGH

### VR-DATASET-005: Format Validation
- **Rule**: Data format must be supported
- **Supported Formats**: csv, json, parquet, sql, excel, avro, orc
- **Implementation**: ENUM constraint
- **Error Message**: "Unsupported data format"
- **Severity**: HIGH

### VR-DATASET-006: Storage Location Validation
- **Rule**: Storage location must be valid
- **Valid Locations**: s3://*, gs://*, /local/path/*, database://connection_string
- **Implementation**: Regex pattern validation
- **Error Message**: "Invalid storage location"
- **Severity**: HIGH

### VR-DATASET-007: Access Date Validation
- **Rule**: Last accessed date must be <= current date
- **Implementation**: CHECK constraint: last_accessed <= NOW()
- **Error Message**: "Last accessed date cannot be in the future"
- **Severity**: MEDIUM

---

## EXECUTION_LOG Entity Validation Rules

### VR-LOG-001: Duration Validation
- **Rule**: Duration must be non-negative
- **Implementation**: CHECK constraint: duration_ms >= 0
- **Error Message**: "Duration cannot be negative"
- **Severity**: MEDIUM

### VR-LOG-002: Output Size Validation
- **Rule**: Output size must not exceed 1MB
- **Implementation**: CHECK constraint: LENGTH(output) <= 1048576
- **Error Message**: "Output size exceeds maximum limit of 1MB"
- **Severity**: HIGH

### VR-LOG-003: Action Validation
- **Rule**: Action must be from predefined list
- **Valid Actions**: execute, compile, test, deploy, analyze, debug
- **Implementation**: ENUM constraint
- **Error Message**: "Invalid action type"
- **Severity**: HIGH

### VR-LOG-004: Status Validation
- **Rule**: Status must be from predefined list
- **Valid Statuses**: success, failure, warning, partial
- **Implementation**: ENUM constraint
- **Error Message**: "Invalid status"
- **Severity**: HIGH

### VR-LOG-005: Environment Validation
- **Rule**: Environment must be from predefined list
- **Valid Environments**: development, staging, production
- **Implementation**: ENUM constraint
- **Error Message**: "Invalid environment"
- **Severity**: HIGH

---

## COLLABORATION Entity Validation Rules

### VR-COLLAB-001: User Uniqueness
- **Rule**: Initiator and collaborator must be different users
- **Implementation**: CHECK constraint: initiator_id != collaborator_id
- **Error Message**: "Initiator and collaborator must be different users"
- **Severity**: HIGH

### VR-COLLAB-002: Access Level Validation
- **Rule**: Access level must be from predefined list
- **Valid Levels**: view, comment, edit, admin
- **Implementation**: ENUM constraint
- **Error Message**: "Invalid access level"
- **Severity**: HIGH

### VR-COLLAB-003: End Time Validation
- **Rule**: End time must be >= start time
- **Implementation**: CHECK constraint: ended_at >= started_at
- **Error Message**: "End time cannot be before start time"
- **Severity**: HIGH

### VR-COLLAB-004: Status Transition Validation
- **Rule**: Collaboration status can only transition in specific ways
- **Valid Transitions**:
  - active → (completed|cancelled)
- **Implementation**: Trigger-based validation
- **Error Message**: "Invalid status transition"
- **Severity**: HIGH

### VR-COLLAB-005: Collaboration Type Validation
- **Rule**: Collaboration type must be from predefined list
- **Valid Types**: pair_programming, code_review, data_analysis, brainstorm
- **Implementation**: ENUM constraint
- **Error Message**: "Invalid collaboration type"
- **Severity**: HIGH