diffscope 0.2.1

Function-level git commit analysis tool

DiffScope

Function-level git commit analysis tool. DiffScope helps you analyze Git commits to identify which functions were modified, added, or deleted.

Features

• Analyze GitHub commits at both file and function levels
• Identify exactly which functions were changed in each commit
• Detect function changes including signature, body, and docstring changes
• Supports multiple programming languages using tree-sitter
• Simple API for integration into other tools

Installation

# Clone the repository
git clone https://github.com/yourusername/DiffScope.git
cd DiffScope

# Install dependencies
pip install -r requirements.txt

Usage

Basic Usage

from diffscope import analyze_commit

# Analyze a GitHub commit
result = analyze_commit("https://github.com/owner/repo/commit/sha")

# Print file-level changes
print(f"Files changed: {len(result.modified_files)}")
for file in result.modified_files:
    print(f"- {file.filename}: +{file.additions} -{file.deletions}")

# Print function-level changes
print(f"Functions changed: {len(result.modified_functions)}")
for function in result.modified_functions:
    print(f"- {function.name} in {function.file}: {function.change_type}")

GitHub Authentication

To avoid rate limits, set a GitHub token in your environment:

# Linux/Mac
export GITHUB_TOKEN=your_token_here

# Windows PowerShell
$env:GITHUB_TOKEN="your_token_here"

# Windows CMD
set GITHUB_TOKEN=your_token_here

Running Tests

DiffScope includes a comprehensive test suite with both unit tests and integration tests.

Unit Tests

Run the unit tests (no GitHub API calls):

python -m pytest tests/unit

Integration Tests

Integration tests require the --run-live-api flag to enable tests that make real GitHub API calls:

# Run with a GitHub token to avoid rate limits
export GITHUB_TOKEN=your_token_here
python -m pytest tests/integration --run-live-api

You can also use the provided test helper:

# Run all tests including integration tests
python tests/run_tests.py --all --token=your_github_token_here

Testing with Verbose Output

To see detailed test output including function changes:

python -m pytest tests/integration/test_commit_analysis.py -v -s --run-live-api

Supported Languages

DiffScope currently supports function detection for:

• Python
• JavaScript
• TypeScript
• Java
• C/C++
• Go

Project Structure

src/
├── parsers/          # Function parsing using tree-sitter
├── core/             # Core analysis functionality
├── utils/            # Utility functions and tools
├── models.py         # Data models
└── __init__.py       # Main API

tests/
├── unit/             # Unit tests
├── integration/      # Integration tests
└── samples/          # Test data

Implementation Details

DiffScope implements a sophisticated approach to analyzing Git commits at the function level. This section provides a detailed overview of the implementation architecture and data flow.

Architecture Overview

DiffScope follows a modular architecture with clear separation of concerns:

• Core Analysis Pipeline: Two-phase approach for efficient analysis

  • Phase 1: File-level analysis via GitHub API
  • Phase 2: Function-level analysis with tree-sitter parsing

• Data Models: Three primary data structures

  • CommitAnalysisResult: Container for all analysis data
  • ModifiedFile: Represents file-level changes
  • ModifiedFunction: Represents function-level changes

• Language Support: Tree-sitter integration for accurate parsing

  • Language-specific queries for function detection
  • Support for Python, JavaScript, TypeScript, Java, C/C++, Go

Data Flow

The analysis follows a clear pipeline:

1. Input Processing

   • Parse GitHub URL to extract repository and commit information
   • Authenticate with GitHub API using provided token

2. File-Level Analysis (git_analyzer.py)

   • Fetch commit metadata and file changes from GitHub API
   • Identify modified, added, deleted, and renamed files
   • Perform language detection based on file extensions

3. Function-Level Analysis (commit_analyzer.py)

   • For each file, retrieve content before and after changes
   • Filter files based on language support and binary detection
   • Process files differently based on their status (added/modified/deleted)

4. Function Detection (function_detector.py & function_parser.py)

   • Parse code using tree-sitter with language-specific queries
   • Extract function metadata (name, position, content)
   • Compare functions between file versions to detect changes

5. Diff Analysis (diff_utils.py)

   • Parse unified diff format to extract change information
   • Map line numbers between original and new file versions
   • Extract function-specific diffs for detailed change analysis

6. Change Classification

   • Identify function change types:
     • Added, deleted, renamed functions
     • Signature, body, and docstring changes
   • Detect renamed functions using similarity metrics

7. Result Generation

   • Compile comprehensive CommitAnalysisResult with all analysis data
   • Include both file and function-level changes

Key Algorithms

1. Function Change Detection:

   • Extract functions from both old and new versions
   • Match functions by name and location
   • Compare function content to classify changes
   • Use diff analysis to identify specific changes

2. Renamed Function Detection:

   • Identify deleted and added functions across files
   • Compute similarity scores between function pairs
   • Match functions with high similarity scores
   • Apply heuristics to confirm renames vs. new implementations

3. Diff Analysis and Line Mapping:

   • Parse GitHub patch format into structured hunks
   • Map line numbers between original and new files
   • Associate diff hunks with specific functions
   • Handle edge cases like overlapping functions

Error Handling and Robustness

DiffScope implements comprehensive error handling:

• Graceful degradation when GitHub API rate limits are reached
• Robust handling of malformed patches and unexpected code structures
• Skip analysis for unsupported languages and binary files
• Detailed logging for diagnosing issues

Performance Optimizations

• Tree-sitter for efficient code parsing
• Two-phase analysis to avoid unnecessary processing
• Targeted function analysis based on diff information
• Progressive refinement from file-level to function-level details

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Development Setup

1. Clone the repository
2. Install development dependencies: pip install -r requirements-dev.txt
3. Run the tests: python -m pytest

Adding Tests

When adding features, please add corresponding tests:

• Unit tests for isolated functionality
• Integration tests for end-to-end workflows

See the test documentation for more details.

License

MIT License