Documentation for contributors, maintainers, and developers working on GitFlow Analytics.
Complete guide to contributing to GitFlow Analytics:
- Code of conduct and community guidelines
- Issue reporting and feature request process
- Pull request workflow and review process
- Release and versioning procedures
Set up your local development environment:
- Repository setup and dependency installation
- Development tools configuration (linting, testing)
- IDE setup and debugging configuration
- Local testing with sample repositories
Comprehensive testing procedures and standards:
- Unit testing with pytest framework
- Integration testing with real repositories
- Performance testing and benchmarking
- Test data management and fixtures
Code quality improvement tracking and procedures:
- Ongoing refactoring phases and progress
- Code quality metrics and targets
- Refactoring principles and best practices
- Testing strategies for safe refactoring
Code quality guidelines and conventions:
- Python style guide (Black, Ruff, mypy)
- Documentation standards and docstring format
- Error handling and logging patterns
- Performance and security best practices
Official project structure and organization standards:
- Directory structure and file placement rules
- Naming conventions and patterns
- Framework-specific organization
- Temporary files and cleanup policies
Automated release and deployment procedures:
- Semantic versioning with conventional commits
- GitHub Actions CI/CD pipeline
- PyPI publishing and distribution
- Documentation updates and changelog management
Understanding the ML and analysis components:
- Machine learning pipeline architecture
- Training data generation and management
- Model evaluation and performance tuning
- Feature engineering and categorization logic
# Setup development environment
pip install -e ".[dev]"
python -m spacy download en_core_web_sm
# Code quality checks
ruff check src/ tests/
black src/ tests/ --check
mypy src/
# Testing
pytest tests/ -v
pytest --cov=gitflow_analytics --cov-report=html
# Local installation for testing
pip install -e .
gitflow-analytics --versionCore Analysis Engine (src/gitflow_analytics/core/)
- Git repository processing and commit analysis
- Developer identity resolution and consolidation
- Caching system and performance optimization
Data Extraction (src/gitflow_analytics/extractors/)
- Commit categorization (rule-based and ML)
- Ticket reference extraction and parsing
- Story point and project management integration
ML Pipeline (src/gitflow_analytics/qualitative/)
- spaCy-based natural language processing
- Commit classification with confidence scoring
- Pattern learning and model improvement
Report Generation (src/gitflow_analytics/reports/)
- CSV, JSON, and Markdown report formats
- Template-based narrative generation
- Data visualization and insight generation
Integrations (src/gitflow_analytics/integrations/)
- GitHub API client and organization discovery
- JIRA and project management platform adapters
- External tool integration patterns
- Test individual functions and methods in isolation
- Mock external dependencies (GitHub API, file system)
- Focus on edge cases and error conditions
- Maintain >80% code coverage
- Test complete workflows with sample repositories
- Validate report generation end-to-end
- Test configuration parsing and validation
- Performance testing with large datasets
- Automated linting and type checking in CI
- Manual testing with diverse repository types
- Documentation accuracy verification
- Security scanning and dependency updates
GitFlow Analytics follows a modular architecture:
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ CLI Interface │────│ Core Analyzer │────│ Report Writers │
└─────────────────┘ └──────────────────┘ └─────────────────┘
│ │ │
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Config Loader │ │ Data Models │ │ Export Formats │
└─────────────────┘ └──────────────────┘ └─────────────────┘
│ │ │
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Integrations │────│ ML Pipeline │────│ Cache System │
└─────────────────┘ └──────────────────┘ └─────────────────┘
- Modularity: Clear separation of concerns
- Testability: Dependency injection and mocking support
- Performance: Caching and batch processing
- Extensibility: Plugin architecture for new integrations
- Reliability: Comprehensive error handling and validation
- Issue Creation: Discuss feature requirements and design
- Branch Creation: Create feature branch from main
- Implementation: Write code following standards
- Testing: Add comprehensive tests for new functionality
- Documentation: Update relevant documentation
- Review: Submit pull request for code review
- Integration: Merge after approval and CI success
- Reproduction: Create minimal test case demonstrating issue
- Root Cause: Identify underlying cause through debugging
- Fix Implementation: Minimal change to resolve issue
- Test Coverage: Add test preventing regression
- Validation: Verify fix resolves original issue
- Measurement: Profile code to identify bottlenecks
- Analysis: Understand performance characteristics
- Optimization: Implement targeted improvements
- Benchmarking: Validate performance gains
- Documentation: Update performance guidelines
- System Overview - High-level architecture
- Data Flow - Processing pipeline design
- Design Documents - Technical decision records
- Configuration Guide - Complete feature overview
- GitPython: Git repository interaction
- PyGitHub: GitHub API integration
- spaCy: Natural language processing
- pandas: Data analysis and manipulation
- SQLAlchemy: Database ORM and caching
- GitHub Issues: Bug reports and feature requests
- GitHub Discussions: Design questions and community help
- Pull Requests: Code review and collaboration
- Documentation: Shared knowledge and best practices
- Code Review: Ensure quality and consistency
- Release Management: Version planning and deployment
- Community Support: Help users and contributors
- Architecture Decisions: Guide technical direction
- Architecture - System design details
- Design Documents - Technical decision records
- Reference - API and configuration specifications
- Deployment - Operations and production setup
Ready to contribute? Start with the Contributing Guide and Development Setup!