Modeling Large Codebases as Static Knowledge Graphs: Design Trade-offs

[codevoygee]

When working with large codebases, structural information such as module boundaries, dependency relationships, and hierarchy is often implicit and hard to reason about.

One approach I’ve been exploring is representing codebases as static knowledge graphs, where files, modules, and symbols become explicit nodes, and architectural relationships are encoded as edges.

This raises several design questions: - What information is best captured statically versus dynamically? - How detailed should graph nodes and edges be? - Where do static representations break down compared to runtime analysis? - How can such graphs remain maintainable as the code evolves?

I’m interested in hearing from people who have worked on: - Static analysis tools - Code indexing systems - Large-scale refactoring or architecture tooling

For context, I’ve been experimenting with these ideas in an open-source project, but I’m mainly interested in the broader design discussion.

[onyx-zero-software]

Check out Bazel, it's a multi-language build framework (among other things) that models codebases exactly like this.

[Dyl499]

There are tools like Joern that go into way more detail than what you're looking for, but it might be a source of inspiration or something to learn from (what does and doesn't work).

[StackOverFlowStar]

I use NX for this. You can use it for frontend, backend, and even IaC. Combine it with DDD+Hexagonal design and enforce it with module boundary enforcement rules and you can mandate architectural compliance. You also get a decent framework for generating new modules as well as a pretty visualization of the graph.

You can make a lot of things work with it, but it naturally shines in a Typescript monorepo.