Potential Issues Identified
The total number of potential issues identified by AI Review across all issue categories during the selected timeframe.
Updated
by Steven Silverstone
Potential Issues Identified is the total number of issues detected by AI Review across all categories during the selected timeframe.
What this metric shows
This metric represents the total volume of findings generated by AI Review, including bugs, security issues, performance concerns, readability issues, maintainability issues, and scope-related gaps.
Why it matters
- Provides a high-level view of overall code quality signals.
- Helps identify trends in issue volume over time.
- Serves as a baseline for evaluating resolution rates and AI impact.
Interpretation tip
AI Review is intentionally over-inclusive. Not all identified issues represent confirmed defects. Use this metric as a directional signal rather than an exact count of problems.
AI Review is intentionally over-inclusive. Not all identified issues represent confirmed defects. Use this metric as a directional signal rather than an exact count of problems.
How to use it
Use this metric to monitor trends in overall issue volume. Compare it with Issues Resolved to evaluate how effectively findings are being addressed, and drill into specific categories to understand the nature of the issues.
Related metrics
How did we do?
Performance Issues Identified
Readability Issues Identified
