how do you make an LLM work on the AST level? do you just feed a normal LLM a text representation of the AST, or do you make an LLM where the basic data structure is an AST node rather than a character string (human-language word)?
Knowing this raises the question, which is better to feed an LLM source code of ASTs?
The answer is really it depends on the use case, there are tradeoffs. For example keeping comments intact possibly gives the model hints to reason better. On the other side, it can be argued that a pure AST has less noise for the model to be confused by.
There are other tradeoffs as well. For example, any analysis relating to coding styles would require the full source code.
Babel first parses the code to AST, and for each variable the tool:
1. Gets the variable name and surrounding scope as code
2. Asks the LLM to come up with a good name for the given variable name, by looking at the scope where the variable is
3. Uses Babel to make the context-aware rename to AST based on the LLM's response