Before You Merge AI-Generated Code, Ask These 12 Questions
I've merged plenty of AI-generated code that was genuinely fine. I've also caught myself almost merging code that looked fine and wasn't, because it read like something a competent person wrote and my brain filled in the rest. Over the last year I've settled into a rough set of questions I run through before approving anything I didn't write line by line myself, generated or not. Here they are,…
Before merging AI-generated code, consider these 12 critical questions to ensure its quality and suitability:
1. What specific problem does this code address? AI-generated code may focus on the literal prompt rather than the intended solution. Verify if it resolves the right issue.
2. Do I fully understand the implementation? Go line by line, explaining the function in simple terms. If you struggle with any part, that's the area needing closer review.
3. What assumptions are being made? AI code often assumes data structure, order, or normalization without explicit checks. Identify any untested assumptions in the implementation.
4. How does it handle bad input? Bad input is a certainty over time. Test with unexpected inputs like empty strings, wrong types, or extreme values to see how it behaves.
5. What happens when an external service fails? Generated code often assumes flawless external service responses. Consider timeouts, error handling, and fallback mechanisms for successful and failed scenarios.
6. Are permissions properly enforced? Differentiate between having an authentication check and verifying the correct permissions for a resource. Ensure only authorized access is granted.
7. Is sensitive data exposed? Review logs, error messages, and API payloads for any leakage of sensitive information. Avoid exposing internal structures or file paths in production.
8. Is the code more complex than necessary? AI-generated code might overcomplicate simple problems with unnecessary abstractions or generic solutions. Strive for simplicity while maintaining correctness.
9. Are the tests meaningful? Generated tests often merely confirm existing behavior without ensuring correct functionality. Evaluate if the tests cover critical edge cases and potential failures.
10. Does it align with existing code conventions? New code should fit seamlessly into the existing codebase. Check for consistency in error handling, logging, HTTP clients, and other conventions.
11. How does it perform under unusual load or concurrency? AI-generated code may not handle concurrent access or high loads well, especially when dealing with shared state or caching mechanisms.
12. Can the next developer understand this without my help? The ultimate test is whether a new developer can grasp the purpose and functionality of the code independently, without relying on your previous knowledge or context.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — it may contain errors, so check the original before relying on it.
