Git Repository Analytics

Part of the worlds-biggest-software-project initiative.

An AI-native, open-source platform for turning Git history into actionable engineering insight — contribution patterns, bus factor, and code churn without per-developer SaaS pricing.

Git Repository Analytics mines commit history to surface the signals engineering leaders actually need: who owns what, where churn is concentrated, and which parts of the codebase carry the most organisational risk. It is built for engineering managers, platform teams, and CTOs who want CodeScene-class temporal analysis without enterprise contracts or developer-surveillance optics.

Why Git Repository Analytics?

Incumbent leaders such as CodeScene and LinearB charge premium per-developer pricing ($15–$40/dev/month, with enterprise contracts reaching $50K–$200K/yr), pricing out smaller teams from sophisticated repository intelligence.
Foundational open-source tools like Code Maat and truegitcodechurn require significant scripting to produce actionable dashboards — there is no polished open-source alternative covering the full analytics surface.
Newer AI-assisted entrants such as Repowise are still alpha-stage with limited scalability, leaving an opening for a production-grade open project.
Bus factor analysis is becoming urgent as engineering team mobility increases, yet most tooling still optimises for activity tracking rather than knowledge-concentration risk.
Per-developer SaaS pricing models also carry surveillance optics (as seen with Waydev), pushing teams toward self-hosted, transparent alternatives.

Key Features

Repository Intelligence (MVP)

Commit history analysis and statistics
Contributor metrics and rankings
Code churn and change frequency tracking
Repository health scoring
Branch and merge analysis
File and module level statistics

Visualisation & Reporting

Interactive visualization dashboard
Historical trend tracking
Export and reporting

Team & Process Analytics (v1.1)

Bus factor analysis (knowledge concentration)
Code review efficiency metrics
Merge time and PR cycle analysis
Developer productivity tracking
Bottleneck identification
Team collaboration metrics

Risk & Anomaly Detection

File stability and risk assessment
Automatic anomaly detection
Trend forecasting

Forward-Looking Capabilities (Backlog)

Developer well-being and burnout prediction
Knowledge transfer tracking
Team dynamics visualization
Architectural evolution tracking
Developer onboarding difficulty prediction
Integration with HR and management systems

AI-Native Advantage

AI lets the platform go beyond raw metrics. It can combine bus factor, recent churn, and service criticality to flag the files at highest organisational risk; generate plain-English repository health summaries for executives ("three engineers own 80% of the payment service"); and auto-produce onboarding guides from commit history, co-change patterns, and module ownership. AI also enables anomaly detection on commit patterns indicating technical-debt accumulation, and predicts delivery risk for in-flight features by correlating current churn with historical patterns preceding delayed or incident-causing releases.

Tech Stack & Deployment

The project targets both self-hosted and cloud deployment, prioritising privacy-first on-premise use for organisations that cannot send source history to a third-party SaaS. It aligns with established engineering standards including DORA Metrics (lead time, deployment frequency), the SPACE Framework's Activity dimension, Conventional Commits for semantic commit analysis, and OpenSSF Scorecard for security-health overlap. Efficient large-repository analytics depends on direct work against Git internals (packfiles, refs).

Market Context

Engineering intelligence (DORA, Git analytics, developer productivity) is estimated at USD 1.2 billion in 2025, growing at roughly 30% CAGR. Incumbent pricing spans $15–$40 per developer per month for tools like LinearB and Waydev, with enterprise platforms reaching $50K–$200K/yr. Primary buyers are engineering managers tracking team health, VPs of Engineering reporting to boards, platform teams identifying knowledge silos, and CTOs assessing acquisition-target codebase health.

Project Status

This project is in the research and specification phase.
Contributions, feedback, and domain expertise are welcome.

Contributing

We welcome contributions from developers, domain experts, and potential users. See CONTRIBUTING.md for guidelines.

Important: All contributions must be your own original work or clearly attributed open-source material with a compatible licence. Copyright infringement and licence violations will not be tolerated and will result in immediate removal of the offending contribution. If you are unsure whether a piece of code, text, or other material is safe to contribute, open an issue and ask before submitting.

Licence

Licence to be determined. See discussion for context.