CodeHealth-AI Documentation
Complete Repository Analysis Methodology
This document provides a comprehensive explanation of how CodeHealth-AI evaluates GitHub repositories. It covers the theoretical foundations of each metric, the exact computational logic used in the analysis, and the practical implications for repository health assessment.
CodeHealth-AI evaluates repositories across four primary dimensions, each contributing to the overall assessment.
Dimensional Breakdown
Code Quality (45% weight)
- Static analysis of file-level metrics
- Maintainability assessment
- Complexity evaluation
- Technical debt quantification
Development Activity (25% weight)
- Commit frequency and patterns
- Development velocity trends
- Contributor consistency
- Recent development momentum
Bus Factor Risk (15% weight)
- Contributor concentration analysis
- Knowledge distribution assessment
- Project sustainability evaluation
Community Engagement (15% weight)
- External visibility metrics
- Community interest indicators
- Project popularity signals