AI-suggested code can look coherent while changing behavior outside the requested feature. Review it as a patch from a contributor whose assumptions you cannot see: understand the diff, trace what it calls, and verify the behavior that matters.
1. Save the exact change and compare it
Keep the original version available, then inspect a focused diff. The Toolsnix Difference Checker can make text changes easier to scan, but a text diff does not understand program behavior. Use Git or your editor for project changes so you can see filenames, context, and deleted lines.
Look for more than the requested edit: changed defaults, new dependencies, widened permissions, altered error handling, generated files, and unrelated formatting. Ask what each changed line enables.
2. Trace the path from input to effect
Follow the new value through validation, transformation, and use. Check who can supply it, whether it is trusted, and what happens when it is absent or malformed. For a database write, file operation, network request, or permission change, inspect the called function as well as the visible diff.
A useful review question is: “What is the smallest input that would make this code do something unintended?” Write that case down before testing.
3. Build a small behavior table
- Ordinary input: A valid supported value should produce the intended result.
- Boundary input: An empty, minimum, maximum, or missing value should trigger the documented fallback or validation.
- Invalid input: A wrong type or malformed value should fail safely and clearly.
- Untrusted input: User-controlled text or identifiers should remain inside their intended context.
Choose cases that match the feature. Not every code change needs all four, but every consequential assumption should have evidence.
4. Validate the behavior, not the confidence
Run the narrowest relevant test first, then the project’s normal checks. Read failures rather than asking the assistant to “fix” them blindly. If the patch changes a public interface, check its callers. If it changes a security boundary, ask someone familiar with that area to review it.
Formatting and compilation answer limited questions. Passing them does not show that requirements are met or that the change is safe.
FAQ
Is a green test suite enough?
It is useful evidence for the cases the suite covers. Check whether the changed behavior and important edge cases are actually represented.
What should I do with a large AI-generated diff?
Break it into understandable pieces, remove unrelated edits, and review each piece against a stated requirement. Do not approve a change you cannot explain.
Can a difference checker review code quality?
It can reveal textual changes. It cannot determine whether a change is correct, secure, or compatible with the rest of the application.