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arXiv · 2608.07026

PyFlow: An Inter-procedural Static Analysis Framework for Python

Zinan Gu·Haoxiang Yan·Peisen Yao
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Abstract

Static program analysis infers program properties automatically. Yet precise interprocedural analysis remains challenging, and dynamically typed languages amplify the difficulty. Python is particularly problematic: dynamic dispatch, first-class functions, metaprogramming, pervasive exceptions, and an object model based on descriptors and attribute-driven lookup collectively impede precise reasoning. We present PyFlow, a generic IFDS-based static-analysis framework for Python. PyFlow provides a multi-stage intermediate-representation pipeline and a generic IFDS solver parameterized by abstract domains. Analysis developers implement only the dataflow semantics; the framework constructs the supergraph, performs fixed-point iteration, and caches summaries. We implement a taint analysis in \pyflow and evaluate it against eight Python SAST tools (DevSkim, Dlint, Bandit, Bearer, CodeQL, Pysa, Semgrep, and Snyk) on the synthetic and real-world benchmarks from a recent ICSE~'26 study. On the synthetic benchmark, PyFlow achieves the best aggregate recall and F1 score among all nine tools. On the real-world benchmark, it attains the highest recall and F1 score while maintaining precision competitive with taint-based engines. We conclude with lessons learned from building IFDS analyses for Python.

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Zinan Gu, Haoxiang Yan, Peisen Yao. 2026-08-07. PyFlow: An Inter-procedural Static Analysis Framework for Python. https://arxiv.org/abs/2608.07026

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