GitStatsDB: AI Code Churn & Engineering Metrics

Is AI Code Churn Stalling Your Team's Velocity?

Many engineering teams introducing GitHub Copilot or other AI coding assistants experience a hidden slowdown: high code churn, PR bottlenecks, and increased defect escape rates. Instead of shipping faster, developers spend more time refactoring unstable AI-generated code.

The Unspoken Engineering Bottleneck: "It's the missing line on the dashboard that most teams haven't drawn yet: if we're producing more code, are we proportionally reviewing more carefully, or are we relaxing review because the volume forced us to? Nobody asks that question out loud. It would slow things down which is something nobody wants right now."

GitStatsDB + NAO offers a developer-centric feedback loop to measure and optimize your team's code health:

• Rework & Regression Rates: Instantly identify modules where AI-generated code is frequently rewritten or reverted.
• PR Cycle & Review Bottlenecks: See where large AI-generated pull requests are stalling your team's code review pipeline.
• Defect Escape Rates: Connect velocity metrics to quality indicators to ensure speed does not compromise reliability.
• Insight Over Surveillance: Designed to track team-level health, velocity, and quality feedback—not to micromanage or rank individuals.

Open Source & Resources

GitStatsDB is fully open source. You can read more about the project's background and setup details on the blog, and check out the repository on GitHub:

• Blog Post: Stop Guessing, Start Measuring Your Git Repository
• GitHub Repository:

Request a GitStatsDB Repository Audit

Provide your work email below to get details on how we can analyze your codebase and configure developer-centric metrics dashboards for your team.