Scalpel: The Python Static Analysis Framework
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Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Feb 2022.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 2; 2025: 2; 2026: 2.
9 papers.
- 2022CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Trainingcited by 15
- 2024PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Softwarecited by 9
- 2022A Library for Representing Python Programs as Graphs for Machine Learningcited by 4
- 2024Code LLMs: A Taxonomy-based Surveycited by 3
- 2023PeaTMOSS: Mining Pre-Trained Models in Open-Source Softwarecited by 1
- 2026RepoRescue: An Empirical Study of LLM Agents on Whole-Repository Compatibility Rescuecited by 1
- 2026Tangent: An Empirical Study of Testing Practices for LLM-Based Agent Applicationscited by 1
- 2025Speculative Automated Refactoring of Imperative Deep Learning Programs to Graph Executioncited by 0
- 2025Can Large Language Models Reason About Complex Execution Paths? An Empirical Study on Pythoncited by 0
- 2026Tangent: An Empirical Study of Testing Practices for LLM-Based Agent Applications9 Aug 2026
- 2026RepoRescue: An Empirical Study of LLM Agents on Whole-Repository Compatibility Rescue1 Jul 2026
- 2025Can Large Language Models Reason About Complex Execution Paths? An Empirical Study on Python23 Nov 2025
- 2025Speculative Automated Refactoring of Imperative Deep Learning Programs to Graph Execution7 Apr 2025
- 2024Code LLMs: A Taxonomy-based Survey11 Dec 2024
- 2024PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software1 Feb 2024
- 2023PeaTMOSS: Mining Pre-Trained Models in Open-Source Software5 Oct 2023
- 2022A Library for Representing Python Programs as Graphs for Machine Learning15 Aug 2022
- 2022CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training4 May 2022