I review PRs primarily for correctness and to look for nonobvious problems. I rarely find buggy code. Sometimes I'll find performance issues. In order to do what I do when reviewing, ChatGPT would need clear specs (yeah right) and a deep understanding of our system, domain, and even our customers.
Of course I'll try it and see how it does. But it struggled and failed to produce what I would consider trivial code when I tried to create a single usable page to spec in react native, so I'm not terribly optimistic.
That's actually how I spent a lot of my time. GPT drafts code of mixed quality, and I spend a lot of time reviewing it.
What's interesting is that GPT isn't trained to produce high-quality code. It's trained as an autocompletion tool. I'd be curious how smart such a system could be if we knew how to engineer it for quality.
Because people will generate more crap and throw it at you as the quality control. They'll have less knowledge overall as they cede more to chat GPT. Also your impact will be in cr, so they'll send you more people. You get faster and more responsive using chat GPT to review, so you'll do more reviews, causing people to send more frequent ones to get your feedback faster. Eventually just hire some interns to toss plausible shit at gpt and then you. Fire the ftes. Done.
GPT can't do full code reviews. GPT is very good at finding improvements, such as making code more functional, bad variable names, etc. In some sense, it's like ESLint or pycodestyle on steroids.
It won't find subtle race conditions, abstraction violations, or deeper issues.
I find it makes code reviews shorter and more productive, so perhaps you'll spend 10% of your time instead of 60%. You won't spend 0% of your time, though.
