I definitely agree that there's a lot of research happening in this space, and the false positive issue is a significant hurdle. From my own research and experimentation, I have also seen how challenging it is to get LLM-powered tools to consistently find real.

Our approach with Jazzberry is specifically focused on the dynamic execution aspect within the PR context. I am seeing that by actually running the code with the specific changes, we can get a clearer signal about functional errors. We're very aware of the need to demonstrate our ability to find those high-severity/exploitable bugs you mentioned, and that's a key metric for us as we continue to develop it.

Given your background, I'd be really interested to hear if you have any thoughts on what approaches you think might be most promising for moving beyond the false positive problem in AI-driven bug finding. Any insights from your work at MIT would be incredibly valuable.