How to review AI-generated code before release
A practical review checklist for AI-assisted software: trace the user journey, check authorization and data writes, test failure paths, and plan recovery.
By Nicholas Howell · Full Stack Technologies ·
Review the behavior the change introduces
Treat AI-generated code as a proposed implementation that needs the same scrutiny as any other change. A successful build confirms that the project can compile. It does not confirm that the application meets its requirements or protects the right data.
Start by identifying what changed for a user. Trace that journey through the interface, request handler, business logic, and storage. Read the callers and the surrounding code so that a locally plausible change is checked against the system it joins.
Check authorization where the data is accessed
Signing in establishes identity. Authorization decides which records and actions that identity is allowed to access. A hidden button in the interface does not establish that boundary.
Inspect the server-side handler and data query. Ask what happens if someone changes a record identifier, sends the request directly, or uses another account. Check tenant ownership and privileged actions explicitly. Never paste real credentials or private customer records into a review prompt.
Follow every important write and failure path
For a write operation, identify what is validated, what can change, and what happens when the operation only partly succeeds. A retry must not silently duplicate a charge, message, or record. Where several changes must succeed together, examine how consistency is maintained.
Check error handling at external services and storage boundaries. The application should distinguish a successful operation from one that failed or needs attention. Avoid swallowing an error and presenting a success state simply because the generated code supplied a convenient fallback.
Choose tests that could catch a real defect
Use a small set of checks tied to the behavior at risk. For a protected report, verify the expected result for an allowed account and denial for an account without access. For an import, include malformed input and a repeated submission.
Read the test itself. A test that repeats the same calculation as the implementation can reproduce the same mistake. Prefer expected results derived from the requirement or a small example you can calculate independently. Run the existing relevant checks as well as the new ones.
Inspect what reaches the browser and logs
Look for secrets in client bundles, sensitive fields returned unnecessarily by APIs, and private information written into logs. Check that new dependencies and scripts are actually needed and that generated files do not introduce unrelated changes.
Review the diff after running tooling. Formatting, scaffolding, and code generation can obscure a small behavior change inside a much larger patch. Remove unrelated changes where possible so the reviewer can see the decision being made.
Define how to release and recover
Before release, know which configuration, permissions, and migrations the change requires. Decide how you will observe whether the core journey still works. For data changes, establish a recovery approach that preserves user data; reverting application code alone may not undo a migration.
If you cannot explain the important control flow, reduce the change or investigate it before accepting it. AI assistance is useful when the resulting work remains understandable to the people responsible for operating it.
- Discuss a codebase review
- See Timetrack’s audit trail and reporting design
- Next: repair or rewrite an existing application?