Refactoring is really rather well defined. It's " just transformations that are invariant w.r.t. the outcome". The reason they are hard to automate is that 'invariant w.r.t. the outcome' is a lot more lenient than most semantic models van handle. But this kind of well-defined task with a slight amount of nuance (and decently checkable) seems pretty well-suited to an LLM.

At least for the linux kernel, qemu and other large c projects, this is a solved problem with coccinelle[1]. Compared to AI, it has the added benefit of not doing incorrect changes and/or hallucinating stuff or promt injections or ...

I guess you could use AI to help create a coccinelle semantic patch.

[1] https://en.wikipedia.org/wiki/Coccinelle_(software)

The part coccinelle does is the part GPT is good at, the problem is neither of them actually reason about the code

I'm genuinely confused about your point of view. Have you tried refactoring with GPT-4?

I have been refactoring code using gpt-4 for some months now and the limiting factor have been the context size.

GPT-4 turbo now have 128k context and I can provide it with larger portions of the code base for the refactors.

When we have millions of tokens of context, based on what I'm experiencing now, I can see that a refactoring like the one made in ffmpeg would be possible. Or not? What am I missing here?