DAG-Based Patch Format Specification

Overview

A patch format designed for machine consumption and LLM processing that represents code changes as a directed acyclic graph of transform operations rather than line-based diffs.

Core Architecture

Base Representation

Tar archive as baseline container for file state
Preserves file structure, metadata, and binary content
Content-addressable storage for deduplication

Delta Encoding

Changes expressed as operations on the tar archive
Operations form a DAG showing true dependencies
No artificial sequential ordering

Transform Operations

Primitive Operations

Copy - Duplicate content within or across files
Move - Relocate content, preserving identity
Delete - Remove content
Reorder - Change sequence without modification
String Replace - Regex-based substitution patterns
Whitespace - Isolated formatting changes (indentation, line endings, trailing spaces)
Binary Delta - Fallback for arbitrary changes

Operation Properties

Each operation node contains:

Unique identifier (content-addressed hash)
Operation type
Source and target references
Dependencies (edges in DAG)
Metadata (optional: intent, semantic tags)

DAG Structure

Dependency Model

Nodes represent operations
Edges represent true causal dependencies
Operation B depends on A only if B requires A’s output state
Independent operations have no edges between them

Benefits

Parallelization - Apply independent branches concurrently
Partial Application - Cherry-pick subgraphs safely
Conflict Detection - Identify incompatible dependencies
Merge Intelligence - Combine patches via graph union
Causality Preservation - Explicit ordering only where required

Auto-Regression Algorithm

Factorization Process

Given a large commit (before/after state):

Whitespace Isolation - Extract all formatting changes first
Pattern Detection - Identify repeated transformations (renames, refactors)
Operation Discovery - Find minimal set of transforms
Dependency Analysis - Build DAG from causal relationships
Optimization - Minimize total description length

Optimization Metrics

Description Length - Total bytes to represent all operations
Consistency - Semantic coherence of operation groupings
Reusability - Favor decompositions matching known patterns
Compression Ratio - Operations vs equivalent binary delta

Use Cases

LLM Integration

Understand semantic intent of changes
Generate similar refactoring patterns
Compose new patches from operation libraries
Automated code review with causal reasoning

Tooling Applications

Intelligent merge conflict resolution
Parallel patch application
Change impact analysis
Pattern-based code search
Incremental verification and bisection

Version Control

Efficient storage (compression via transform reuse)
Fast cherry-picking (subgraph extraction)
Better blame tracking (operation-level attribution)
Semantic diff comparisons

Implementation Considerations

Format Properties

Binary-safe throughout
Content-addressable operations enable deduplication
Self-contained (references to file states included)
Extensible (new operation types can be added)

Tool Responsibilities

Format is substrate only. Intelligence lives in tools:

Writers - Generate optimal factorizations
Readers - Apply operations, resolve dependencies
Mergers - Combine DAGs, detect conflicts
Analyzers - Extract patterns, compute metrics

No formal grammar required. Operations are data, not a language to parse.

Example Structure

 Patch {
  base_tar: <content-hash>
  operations: [
    {
      id: <hash-1>
      type: "whitespace"
      intent: "normalize-indentation"
      semantic_tags: ["formatting", "non-functional"]
      description: "Normalize Python indentation to 4 spaces"
      author: "system"
      timestamp: "2024-01-01T00:00:00Z"
      confidence: 1.0
      reversible: true
      breaking_change: false
      affected_domains: ["style"]
      scope: ["**/*.py"]
      transform: <normalize-indentation>
      depends_on: []
    },
    {
      id: <hash-2>
      type: "move"
      source: "utils.py:Foo"
      intent: "refactor-module-organization"
      semantic_tags: ["refactoring", "structural"]
      description: "Move Foo class to helpers module for better organization"
      author: "developer"
      timestamp: "2024-01-01T00:00:00Z"
      confidence: 0.95
      reversible: true
      breaking_change: false
      affected_domains: ["architecture", "imports"]
      target: "helpers.py:Foo"
      depends_on: []
    },
    {
      id: <hash-3>
      type: "string_replace"
      pattern: "import.*Foo.*from utils"
      intent: "update-imports-after-move"
      semantic_tags: ["refactoring", "import-update"]
      description: "Update import statements to reflect Foo class relocation"
      author: "system"
      timestamp: "2024-01-01T00:00:00Z"
      confidence: 0.98
      reversible: true
      breaking_change: false
      affected_domains: ["imports"]
      replacement: "import Foo from helpers"
      scope: ["src/**/*.py"]
      depends_on: [<hash-2>]
    },
    {
      id: <hash-4>
      type: "binary_delta"
      target: "main.py"
      intent: "logic-update"
      semantic_tags: ["feature", "logic-change"]
      description: "Update main.py logic to use refactored Foo class"
      author: "developer"
      timestamp: "2024-01-01T00:00:00Z"
      confidence: 0.85
      reversible: true
      breaking_change: false
      affected_domains: ["logic", "behavior"]
      delta: <compressed-diff>
      depends_on: [<hash-3>]
    }
  ]
}

Future Extensions

Semantic fingerprinting - Build libraries of common patterns
Diff-of-diffs - Compare patches by DAG structure
Probabilistic operations - LLM-suggested transforms with confidence scores
Verification hooks - Checkpoint validation at DAG nodes
Cross-repository patterns - Reuse transforms across codebases
Complete Patch with Metadata

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