Thread

1. n4r9 · 1790545492 · focus · HN ↗

   There&#x27;s been a lot of talk about the purpose of code review recently. It makes sense in the face of AI. Heres a link that was submitted a little while ago: <a href="https:&#x2F;&#x2F;mathstodon.xyz&#x2F;@mjd&#x2F;115096720350507897" rel="nofollow">https:&#x2F;&#x2F;mathstodon.xyz&#x2F;@mjd&#x2F;115096720350507897

   And in response I wrote a non-exhaustive checklist of things that a code review can look for:

   - Does it functionally achieve what it sets out to (as per tacker issue or PR description)?

   - Does it have extraneous code? Leftover debug prints, private API keys etc...

   - Does it have any obvious defects? Memory leaks, un-handled edge cases, security flaws, obsolete API calls, etc...

   - Could it be more understandable? Add&#x2F;remove abstractions, better variable&#x2F;method names, more&#x2F;less functional etc...

   - Is the style consistent with the codebase and&#x2F;or style guidelines?

   - Are there obvious performance improvements? Hashset instead of list, lazy evaluations, etc...

   - Is it sufficiently well tested?

   I think LLMs are okay at most of these, and worst at the first.

   1. Boxxed · 1790555196 · focus · HN ↗

      Code review also transfers knowledge to the reviewers!

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