Typify: A Lightweight Usage-driven Static Analyzer for Precise Python Type Inference

TL;DR

Typify is a lightweight static analysis tool that infers precise Python types based on usage patterns, avoiding large datasets and deep learning, and effectively handling complex, real-world codebases.

Contribution

It introduces a novel, usage-driven static analysis approach combining symbolic execution and retrieval systems for accurate type inference without training data.

Findings

Typify matches or surpasses deep learning-based tools like Type4Py and HiTyper.

It outperforms industry-standard static type inference tools such as Pyre.

The approach is efficient and scalable for large, evolving Python projects.

Abstract

Python's dynamic type system, while offering significant flexibility and expressiveness, poses substantial challenges for static analysis and automated tooling, particularly in unannotated or partially annotated codebases. Existing type inference approaches often depend on existing type annotations or on deep learning models that require extensive training corpora and considerable computational resources, resulting in limited scalability and reduced interpretability. We introduce Typify, a lightweight, usage-driven static analysis engine designed to infer precise and contextually relevant type information without relying on statistical learning or large datasets. Typify integrates symbolic execution with iterative fixpoint analysis and a context-matching retrieval system to propagate and predict type information across entire projects. By constructing and traversing dependency graphs in an execution-aware manner, Typify accurately connects function calls to their definitions and infers usage-based type semantics, even in complex, interdependent modules. We evaluate Typify on a diverse corpus of real-world Python repositories, including the ManyTypes4Py and Typilus datasets, benchmarking its effectiveness in predicting types of variables, arguments, and return statements. Results from the evaluation show that Typify consistently matches or surpasses state-of-the-art deep learning-based systems such as Type4Py and HiTyper, as well as industry-standard static type inference tools like Pyre. Our findings demonstrate that usage-driven, retrieval-based inference can match or exceed the accuracy of data-driven methods, offering a practical, interpretable, and computationally efficient alternative for large and evolving Python codebases.