Overview
- AI refactors can pass visual review and standard tests yet change edge-case behavior, so the author recommends first writing characterization tests that capture current outputs for normal, boundary and weird inputs.
- The recommended four-step workflow is: write characterization tests to freeze observable behavior, request a refactor from an AI model, run the full test suite on an isolated reproducible server, and decide using a rule that combines test results with diff size.
- The coverage shows practical tooling: the author used MonkeyCode’s free model to propose refactors and its free server to run a verification script that fetches the branch, shows changed files, installs dependencies, and runs tests in a clean environment.
- Decisions follow a simple table: accept small diffs that pass tests, require human review for large passing diffs, and reject any refactor that breaks characterization tests because failures point exactly to changed behavior.
- The method preserves legacy quirks during refactors and speeds safe, small changes, but it is not a proof of correctness, cannot replace human code review or formal verification, and should not be used for safety-critical or nondeterministic code.