[hankbond]

Also I keep seeing solutions in this space that are doing inheritance and call stack dependency linkage, but I haven't seen the same level of exploration into data lifetime dependency. Not lifetime in the way it exists in Rust (to my knowledge), but like including when you copy data and transform that copy. The motivation is "if I change this variable, enumerate all the areas that change would propagate to". The idea is similar, to evaluate blast radius of modifications. Ideally something like this could make refactoring more token efficient and consistent as well.

I don't know if you can reliably do that with static analysis tho. I would be interested in some sort of debug attachment like process that does a code coverage type evaluation. If you can't tell this is at least on the edge of (if not past) my depth of expertise

[rohanucla]

This is a really interesting direction, you're essentially talking about data flow or taint analysis, where you track how a value propagates through copies and transformations rather than just following call edges. Honestly pure static analysis gets you partway there but it hits real limits once you run into dynamic dispatch, runtime branching, or serialization boundaries where data gets written somewhere and read back in a completely different part of the codebase.

We're on the structural side right now with call graphs and dependency edges, but a hybrid approach that combines the static graph with runtime instrumentation to fill in the gaps is definitely something I'd love to explore. Thanks for the feedback.

[hankbond]

https://en.wikipedia.org/wiki/Taint_checking

I'm sorry for distracting from your engaging and thoughtful reply but I can't help but giggle at the name of this concept.

[insensible]

You think it’s funny but it taint.

[rohanucla]

haha definitely!