code-review-and-quality

Multi-axis code review systematically evaluates pull request changes across correctness, readability, architecture, security, and performance. It enforces small change sizing, requires tests, and produces categorized findings with severity labels before merging.

Reviewing AI-generated code requires verifying tests, spec alignment, and risk mitigations across five axes: correctness, readability, architecture, security, and performance. The process enforces change sizing and categorizes findings by severity to reduce regressions.

Code review checklists enforce security and performance discipline by systematically evaluating implementation across defined axes, verifying the verification story, and categorizing findings with severity labels to mitigate architectural debt and vulnerabilities.

Yes, automated review checklists apply to refactor audits and post-implementation reviews. They assess correctness, readability, architecture, security, and performance, while guiding splitting strategies for large changes to ensure reviewability.

Splitting large pull requests involves enforcing small, reviewable changes when possible. The review process requires tests and a clear verification plan, guiding splitting strategies to maintain code quality and reduce regression risk before merging.

Code review requires a verification plan and tests to validate the verification story. This ensures spec alignment, confirms risk mitigations across the five review axes, and enforces security and dependency discipline before changes are merged.