I wrote a blog post for the project this week detailing some of the challenges the QEMU project, in common with other FLOSS projects, are seeing with AI generated bug reports. The dilemma being that the LLM’s have got to the point they generate better bug reports than a lot of humans do.
I do run a bot that can do the initial triage, it doesn’t solve the problem of bugs stacking up in unmaintained areas of the code. I have experimented with auto-fixing the bugs with an LLM but currently the project policy is no AI contributions at all. We are in the process of debating a new policy which might allow for simple fixes to be done with AI.
I didn’t mean fixing them. Just categorizing them. Eliminating duplicates. Marking some as ‘won’t fix’, etc.
Out of an abundance of caution we don’t let the agent close bugs or assign CVEs but tell it to defer to a real human for that. I’ll have to experiment with how we can do a better job of detecting duplicates because I’m sure a fair number of the issues are.
my management wants us to do this using ansible tower (i refuse to t call it the ansible automation platform) and the test cases we setup with it auto repairing systemd configurations and restarting it until it works the way we need it to are working REALLY well.