arXiv:2611.14879v1 Announce Type: new Abstract: Python's dynamic type system, while expressive and flexible, creates significant challenges for static analysis tools in unannotated or partially annotated codebases. Existing approaches either require pre-existing type annotations, or depend on deep learning models that demand large training corpora and significant computational resources. We present Typify, a lightweight, usage-driven static type inference engine that infers precise type information across entire Python projects without annotations or training data. Typify integrates symbolic execution, fixpoint analysis, and context-matching retrieval to propagate types across modules, functions, and recursive call structures. The tool is available as both a command-line interface (CLI) for batch inference, evaluation and downstream integration with other tools, and a Visual Studio Code (VS Code) extension for interactive type assistance. Evaluation on the ManyTypes4Py and Typilus datasets shows that Typify outperforms all static baselines and closely matches state-of-the-art hybrid deep learning approaches, while running approximately 10x faster.
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