gitStream AI Review Findings

LinearB's gitStream AI surfaces review insights that go beyond code completion. These metrics help you identify potential risks and improvement areas across all pull requests reviewed by the AI engine.

Each finding category reflects a different dimension of code quality, allowing engineering leaders and developers to track patterns, uncover root causes, and take proactive steps to improve the health of the codebase.

You can also click the Share icon to generate a direct link to the filtered view for collaboration or documentation purposes.

PRs With Detected Bugs

This metric highlights logic and correctness issues flagged by gitStream, including improper control flow, missing validations, or faulty error handling. These are early indicators of defects that could impact runtime behavior or stability. Investigating frequent bugs may lead to improved review processes, better test coverage, or targeted developer training.

PRs With Detected Security Issues

Tracks the number of times gitStream flagged potential security risks—such as improper input validation, authentication flaws, insecure communication, or access control issues. This metric helps security-conscious teams monitor how effectively secure coding standards are followed across PRs. Consistent findings here may suggest the need for improved guidelines or additional tooling.

PRs With Detected Performance Issues

Highlights code inefficiencies detected by gitStream, such as algorithmic slowdowns, excessive memory use, or inefficient network/data handling. Monitoring this metric helps teams catch potential performance regressions before they impact users. Frequent findings may guide optimization efforts or improve coding standards.

PRs With DetectedReadability Issues

Measures the volume of issues related to code clarity—such as naming conventions, structure, or overly complex logic. High scores here may indicate unclear code that could impact maintainability and onboarding. Teams can use this data to refine style guides or encourage more consistent documentation practices.

PRs With Detected Maintainability Issues

Represents findings that relate to the long-term manageability of the codebase—such as tight coupling, code duplication, poor modularity, or lack of abstraction. Addressing maintainability concerns early can prevent technical debt and improve release agility. This metric enables teams to prioritize structural improvements over time.

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