Thanks! The data artifacts angle is really interesting. in some ways the problem is even harder there because data pipelines have less explicit structure than code, I guess.

Something like https://www.kythe.io/?

Ha, the regex approach is honestly how a lot of people start with this problem and you can get surprisingly far with it until you hit the edge cases around aliased imports, re-exports, and nested scopes where things start falling apart. That's basically why we went with tree-sitter under the hood it gives you the actual parse tree so you don't have to keep patching regex patterns for every new language construct.

I am sorry, should have put up a warning there, but You can do sem unsetup, if you go to the github, you will understand more about the way to reverse it.

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

What I've been more interested in lately is structural intelligence as a field in whole.

Things with LLMs break because our infra was always designed for analyzing lines(tools like grep fuzzy matching) and working on quite small sections of code. LLMs struggle with this in cases when they have to analyze different parts of a codebase they either get too much context where you're throwing whole files at them, or too little where they only see the function in isolation, with no real understanding of how the pieces actually connect to each other.

That's really the gap sem is trying to fill. With sem impact you can give an agent the precise blast radius of a change instead of guessing which files matter, and sem diff --patch lets you enforce that a change only touches specific functions and reject anything that bleeds outside that boundary something that's really hard to do with line-level diffs.

Your testing idea is actually closer than you might think. sem already extracts entity signatures, dependencies, and call graphs, so you could build a harness that gives the test-writing agent only the function signature with its dependency graph and behavioral contract, while withholding the implementation entirely. That would force the agent toward behavioral tests because it literally can't see the internals to mock them. I haven't built this harness myself yet but sem graph and sem inspect expose everything you'd need.

The general principle is that sem gives you a structural map of the codebase to both constrain and validate what the model produces, rather than treating code as flat text and hoping the model figures out the relationships on its own.

Another usecase can be about figuring out dead code present in the codebase.

Edit: Also one last thing because I started working on this while solving the fundamental issue of why merge conflicts were occuring with git, so you might also like the merge drive I open sourced on the same Github org - Weave

Lemme give you an example. when you're working in a 100K-file TypeScript monorepo and you change a utility function that parses API responses. git diff tells you that you changed n lines in that function. What it doesn't tell you is which services, components, and tests actually depend on that function across the repo. You're left grepping for the function name, hoping nobody aliased the import or re-exported it through a barrel file. sem impact gives you that full downstream dependency list in seconds, so you know exactly what to review and test before you ship.

Really glad you've been using it, and yeah that's exactly the direction I've been thinking about. The line diff as the default view in code forges has always felt like an accident of history it definitely was easy to compute, but not what's actually useful for understanding what changed.

Thanks for pointing it out. I agree with you here, my testing process was quite specific to sem's output but also would love any suggestion from you of how you would design the whole testing process for this kind of tool?

I can also give my thought process, because I was more interested in figuring out the model's inherent search results and understanding without sem.

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It can do that, but that's a small slice of what it does. sem parses your codebase into entities (functions, classes, methods) and builds a dependency graph across files.

So instead of line level analysis the whole granularity of seeing changes and tracking thing shifts to entities. It helps in attention mapping of your agent and lets you track the changes faster.

LSPs have been doing it for quite long but using treesitters is faster even tho type awareness is not great with this approach but overall working across multiple languages with a single tool can be quite helpful.

git is actually great, and there are not much of the issues as the world says about it, and the best is to build complimentary layers that makes it even stronger is the best bet I guess.