LLMs seems overkill for this. But of course letting it do general code reviews is not a bad idea. And static code analyzers aren't perfect. Doing this at scale might be a bit tricky though; you'd have to go file by file. I assume Uber might have many millions of lines of code to screen. And maybe their developers aren't that concerned with addressing warnings.

But going file by file might be simple enough. And you can probably screen for important stuff with some decent system prompt and instruct it to check for potential resource leaks and similar issues and produce detailed bug reports with some priority if it finds them and a diff that addresses the issue. Could be a nice use case for agentic AIs to screen code like this.

Besides, gotta start somewhere. It's probably a PoC, for a platform that will eventually handle all sorts of things.

I agree about the limitations, but look from another perspective, is it good use of time asking experienced engineers who get paid 300-500k/year to go and fix these issues? Probably no.

Would it bring them a joy finding simple leaks and fixing them? Probably no again.

Then this tool is a nice addition to the engineers

Edit: Disregard the above, it was too naive.

It cannot leak because of a stronger warranty. In the JLS, it is specified that the try-with-resources track successfull initialisation of AutoClosable resources, not successful assignation. So one try with resources can desugar into the logical equivalent of multiple nested try-catch-finally !