Reviewing AI-Generated Code: A Checklist for Developers
This article provides a step-by-step checklist for reviewing AI-generated code in pull requests, focusing on common failure modes like plausible but wrong logic, hallucinated APIs, and missing error handling.
Why it matters
This checklist provides a structured approach for effectively reviewing AI-generated code, which has unique failure modes compared to human-written code.
Key Points
- 1AI-generated code has different failure patterns than human-written code, requiring a specialized review process
- 2The checklist covers scope, API verification, edge case handling, error path tracing, test coverage, dependency checks, and reading the code backwards
- 3The full review takes about 10 minutes and helps catch issues that could lead to production bugs
Details
The author explains that AI-generated code often exhibits different failure patterns than human-written code, such as plausible but incorrect logic, calls to non-existent APIs, over-engineering, and missing error handling. To address these issues, the author provides a 7-step checklist for reviewing AI-generated pull requests. The steps include: 1) Scope check to ensure the PR only changes what was requested, 2) API verification to confirm imported modules and methods actually exist, 3) Edge case audit to test for handling of empty input, null values, etc., 4) Error path tracing to ensure proper error handling, 5) Test coverage check to look for tests covering failure cases, 6) Dependency check to ensure new dependencies are necessary, and 7) a 'read it backwards' pass to evaluate individual function logic. The author notes that this thorough review process takes about 10 minutes, which is time well spent to catch issues that could lead to production bugs. The checklist is especially important for large PRs or those modifying sensitive areas like authentication and payments.
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