data-substrate-analysis

You can analyze data primitives and state management by running static analysis on source files like types.py, schema.py, models.py, and state.py to classify typing strategies, immutability, and serialization without executing code.

Assess mutation behavior and immutability by scanning state.py and models.py to identify mutation patterns, evaluate lifecycle management of data objects, and recommend safer state-handling approaches for maintainability.

No, this static analysis does not execute code; it scans source files to document serialization strategies like JSON, pickle, or custom methods and outputs a structured report on data object lifecycles.

Yes, you can compare data modeling approaches across frameworks by generating a structured report that outlines typing strategies, mutation patterns, serialization approaches, and recommended practices to guide architectural decisions.

Static analysis cannot execute code or capture runtime behavior, limiting its evaluation to source file structures, typing classifications, and documented patterns rather than actual runtime state mutations or dynamic serialization outputs.