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Scalpel: The Python Static Analysis Framework

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arxiv 2202.11840 v1 pith:L465D7V3 submitted 2022-02-24 cs.SE

Li Li , Jiawei Wang , Haowei Quan This is my paper · ORCID
classification cs.SE
keywords analysisframeworkpythonstaticscalpelconstructionsdedicatedgraph
verification ladder T0 review T1 audit T2 compute T3 formal
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Despite being the most popular programming language, Python has not yet received enough attention from the community. To the best of our knowledge, there is no general static analysis framework proposed to facilitate the implementation of dedicated Python static analyzers. To fill this gap, we design and implement such a framework (named Scalpel) and make it publicly available as an open-source project. The Scalpel framework has already integrated a number of fundamental static analysis functions (e.g., call graph constructions, control-flow graph constructions, alias analysis, etc.) that are ready to be reused by developers to implement client applications focusing on statically resolving dedicated Python problems such as detecting bugs or fixing vulnerabilities.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Can Large Language Models Reason About Complex Execution Paths? An Empirical Study on Python

    cs.SE 2025-11 conditional novelty 6.0 of 10

    State-of-the-art LLMs solve over 60% of complex Python path constraints in test generation, but their path classification is unreliable, especially for infeasible paths.

  2. Combining Type Inference and Automated Unit Test Generation for Python

    cs.SE 2025-07 conditional novelty 6.0 of 10

    A proxy-based type tracing technique integrated into Pynguin infers parameter and return types during test generation and improves branch coverage.

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