If that's true, then I would think the emphasis in code review should be more on test quality and verifying that the spec is captured accurately, and as you suggest, the actual implementation is less important.
If that's true, then I would think the emphasis in code review should be more on test quality and verifying that the spec is captured accurately, and as you suggest, the actual implementation is less important.
You should be planning out the tests to properly exercise the spec, and ensuring those tests actually do what the spec requires. AI can suggest more tests (but be careful here, too, because a ballooned test suite slows down CICD), but it should never be in charge of them completely.
But the more complex bits require human instruction and/or intervention.
>> have a model generate a bunch of tests instead
> wow that's a lot of test code, how will we know it's working correctly?
>> review it
> :face-with-rolling-eyes:
Problem is, the way I've been trained to test isn't exactly antagonistic. QA does that kind of thing. Programmers writing tests are generally rather doing spot checks that only make sense if the code is generally understood and trustworthy. Code LLMs produce is usually broken in subtle, hard to spot ways.