code-review-and-quality

To automate a code review, apply a structured checklist across five axes: correctness, readability, architecture, security, and performance. This process requires severity-labeled feedback and documents resolution status to guide merge decisions.

The best way to review AI-generated code is using a standardized multi-axis evaluation. This assesses correctness, readability, architecture, security, and performance, providing severity-labeled feedback to ensure overall code health improves before merging.

Yes, you can use an automated review checklist for bug fixes and feature forks. The multi-axis evaluation applies to these changes, imposing structured feedback with severity labels and documented resolution status to standardize the merge process.

No, review approval does not require perfect scores. The approval gate passes when overall code health improves, evaluating correctness, readability, architecture, security, and performance to ensure the change benefits the codebase.

Severity-labeled feedback in a code review works by assigning priority levels to issues found across correctness, readability, architecture, security, and performance. This documentation tracks resolution status to guide final merge decisions.

You need a multi-axis code review when standardizing evaluations to reduce human error and review drift. It is required for pull requests, refactors, and bug fixes to ensure improvements across correctness, security, architecture, performance, and readability.