AI Review Metrics

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AI Review Metrics

Understand what each AI Review metric measures, how it appears in dashboards, and how to interpret findings across AI-reviewed pull requests.

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AI Review Coverage

The percentage of pull requests in the selected timeframe that received at least one AI review.

AI Review Metrics Overview

Learn how AI Review works, how to configure it, and how to interpret trends and findings across AI-reviewed pull requests.

AI-Suggested Code Lines Committed

The number of code lines committed using AI Review’s Commit Suggestion option to resolve flagged issues.

Bugs Identified

The number of bug-related issues identified by AI Review, including logic, validation, and error-handling problems.

Issues Resolved

The number of AI-identified issues that were resolved during the selected timeframe, whether by applying an AI suggestion or fixing them independently.

Maintainability Issues Identified

The number of maintainability issues identified by AI Review that may increase technical debt or reduce long-term code health.

PRs Opened

The total number of pull requests created during the selected timeframe across connected repositories.

Performance Issues Identified

The number of performance-related issues identified by AI Review, including inefficient logic, memory use, and slow operations.

Potential Issues Identified

The total number of potential issues identified by AI Review across all issue categories during the selected timeframe.

Readability Issues Identified

The number of readability issues identified by AI Review that affect code clarity, consistency, and ease of understanding.

Reviewed PRs

The number of unique pull requests that received at least one AI review during the selected timeframe.

Scope Issues Identified

The number of scope-related issues identified by AI Review where a pull request may not fully address linked requirements or acceptance criteria.

Security Issues Identified

The number of security-related issues identified by AI Review, such as access control, validation, and insecure implementation patterns.