Performance Issues Identified
The number of performance-related issues identified by AI Review, including inefficient logic, memory use, and slow operations.
Performance Issues Identified is the number of potential performance-related issues detected by AI Review across pull requests during the selected timeframe.
What this metric shows
This metric captures inefficiencies in code that may impact runtime performance. These include issues such as inefficient algorithms, unnecessary computations, excessive memory usage, or suboptimal data handling.
Why it matters
- Helps detect performance risks before they reach production.
- Supports proactive optimization of code and system behavior.
- Reduces the likelihood of performance regressions impacting users.
Not all performance findings require immediate changes. Focus on repeated patterns or issues in critical paths that may have measurable impact.
How to use it
Use this metric to monitor trends in performance-related findings over time. Spikes may indicate changes in code complexity, increased load handling, or areas that require optimization. Investigate high-impact findings to improve system efficiency.
Related metrics
How did we do?
PRs Opened
Potential Issues Identified
