I am playing around with interactive workflow where the model suggests what can be wrong with a particular chunk of code, then the user selects one of the options, and the model immediately implements the fix.
Biggest problem? Total Wild West in terms of what the models try to suggest. Some models suggest short sentences, others spew out huge chunks at a time. GPT-OSS really likes using tables everywhere. Llama occasionally gets stuck in the loop of "memcpy() could be not what it seems and work differently than expected" followed by a handful of similar suggestions for other well-known library functions.
I mostly got it to work with some creative prompt engineering and cross-validation, but having a model fine-tuned for giving reasonable suggestions that are easy to understand from a quick glance, would be way better.