We scored 50k PRs with AI
what we learned about code complexity
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What is We scored 50k PRs with AI?
Key features
AI-powered PR analysis
examines commit history and pull request data to extract patterns about code complexity and shipping velocity
Complexity scoring
categorises changes by complexity level to help you understand what kind of work your team tackles
Velocity metrics
tracks how quickly code moves through review and merging stages
Team performance insights
identifies patterns in how different team members or groups contribute
Historical trend analysis
shows how your engineering velocity and code complexity have changed over time
Pros & cons
Advantages
- Based on real shipping data rather than self-reported estimates or story points
- Helps identify bottlenecks in your code review process
- Provides objective metrics for discussing engineering capacity and team performance
- Freemium model means you can evaluate it without upfront cost
Limitations
- Relies on git history quality; teams with poor commit messages or practices may see less useful analysis
- AI-powered analysis means results depend on the quality of the underlying model and may not capture context that humans understand
Use cases
Engineering managers tracking whether their team's velocity is improving or degrading over quarters
Teams identifying which parts of their codebase are becoming harder to work with
Leaders making decisions about hiring or resource allocation based on actual capacity data
Post-incident analysis to understand whether code quality metrics changed after a significant change
Comparing velocity across different teams or projects within the same organisation
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