Bugs Identified
The number of bug-related issues identified by AI Review, including logic, validation, and error-handling problems.
Updated
by Steven Silverstone
Bugs Identified is the number of potential logic and correctness issues flagged by AI Review during the selected timeframe.
What this metric shows
This metric captures issues that may affect how the code behaves at runtime. These include problems such as incorrect logic, missing validations, improper error handling, or edge cases that could lead to failures.
Why it matters
- Helps detect defects early, before they reach production.
- Highlights patterns in logic-related issues across pull requests.
- Supports improving code review quality and test coverage.
Interpretation tip
AI Review is intentionally over-inclusive. Some findings may be false positives, but are surfaced to reduce the risk of missing critical defects.
AI Review is intentionally over-inclusive. Some findings may be false positives, but are surfaced to reduce the risk of missing critical defects.
How to use it
Use this metric to track trends in potential defects over time. A rising trend may indicate gaps in testing, unclear requirements, or areas where additional review attention is needed.
Related metrics
How did we do?
AI-Suggested Code Lines Committed
Issues Resolved
