Maintainability Issues Identified
The number of maintainability issues identified by AI Review that may increase technical debt or reduce long-term code health.
Maintainability Issues Identified is the number of structural issues detected by AI Review that may impact long-term code maintainability during the selected timeframe.
What this metric shows
This metric captures issues related to how easy the code is to understand, modify, and extend over time. These include problems such as tight coupling, code duplication, poor modularity, lack of abstraction, and overly complex structures.
Why it matters
- Highlights areas that may increase technical debt.
- Helps teams improve long-term code quality and stability.
- Supports better architectural decisions and refactoring efforts.
Not all maintainability findings require immediate action. Focus on recurring patterns or high-impact areas that may slow down development over time.
How to use it
Use this metric to monitor trends in code maintainability. Consistently high values may indicate the need for refactoring, improved coding standards, or better architectural practices. Addressing these issues early can reduce future development friction.
Related metrics
How did we do?
Issues Resolved
PRs Opened
