Yes, but the test code “writes itself” - the path is clear, you just have to fill in the blanks.
Writing the proper product code in the first place, that’s the valuable challenge.
Comment on AI agents wrong ~70% of time: Carnegie Mellon study
zbyte64@awful.systems 2 days agoIt’s usually vastly easier to verify an answer than posit one, if you have the patience to do so.
I usually write 3x the code to test the code itself. Verification is often harder than implementation.
MangoCats@feddit.it 2 days ago
zbyte64@awful.systems 2 days ago
Maybe it is because I started out in QA, but I have to strongly disagree. You should assume the code doesn’t work until proven otherwise, AI or not.
MangoCats@feddit.it 2 days ago
I’ve been R&D forever, so at my level the question isn’t “does the code work?” we pretty much assume that will take care of itself, eventually. Our critical question is: “is the code trying to do something valuable, or not?” We make all kinds of stuff do what the requirements call for it to do, but so often those requirements are asking for worthless or even counterproductive things…
zbyte64@awful.systems 2 days ago
Literally the opposite experience when I helped material scientists with their R&D. Breaking in production would mean people who get paid 2x more than me are suddenly unable to do their job. But then again, our requirements made sense because we would literally look at a manual process to automate with the engineers.
jsomae@lemmy.ml 2 days ago
It really depends on the context. Sometimes there are domains which require solving problems in NP, but where it turns out that most of these problems are actually not hard to solve by hand with a bit of tinkering. SAT solvers might completely fail, but humans can do it. Often it turns out that this means there’s a better algorithm that can exploit commanalities in the data. But a brute force approach might just be to give it to an LLM and then verify its answer. Verifying NP problems is easy.