Scalpel: The Python Static Analysis Framework
- The Auditorium
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- 30 minutes
As the most popular programming language nowadays, it has been pointed out that Python static code analysis has not yet received enough attention from the research and OSS community. For instance, to the best of our knowledge, there is no general static analysis framework proposed to facilitate the implementation of dedicated Python static analyzers (e.g., compared to the Java Soot/WALA framework).
Easy to use and fast to prototyping, what makes Python stand out is bringing challenges to static analysis tasks. To fill this gap, we design and implement Scalpel (A Python Static Analysis Framework) 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. In addition, documentation and the user guide are provided for users.
The objective of the Scalpel framework is to (1) improve Python software quality and (2) support addressing research challenges (e.g. API studies) in software engineering research;
TalkSoftware Engineering & Architecture
Scalpel is a Python Static Analysis Framework. It provides essential program analysis functions for facilitating the implementation of client applications focusing on statically resolving dedicated problems.
We aim to provide Scalpel as a generic Python static analysis framework that includes as many functions as possible (e.g., to easily build inter-function control-flow graph, to interpret the import relationship of different Python modules, etc.) towards facilitating developers to implement their dedicated problem-focused static analyzers.
Scalpel’s Github page has received around 100 stars from researchers and practitioners around the world in only three-month after being made public.