While generative models for code completion are currently popular, code construction is only a small part of software development. Instead, code maintenance spans a much larger proportion of software development. One way to support such activities is through learned program analyses, However, token-based representations of code have been shown to underperform for such tasks.

In this talk, I discuss graph and hypergraph representations of code that can be used with deep learning models for program analyses. Then, I illustrate how such models can be used towards finding and fixing seemingly simple but hard-to-find bugs.

I conclude by discussing open challenges and opportunities in this area.