自动生成静态调用图的Python源代码

Gharib Gharibi, Rashmi Tripathi, Yugyung Lee
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引用次数: 25

摘要

静态调用图是大多数过程间分析和软件理解工具中必不可少的先决条件。然而,缺乏能够自动分析Python源代码并构建其静态调用图的软件工具。在本文中,我们介绍了一个原型Python工具code2graph,它可以自动完成以下任务:(1)分析Python源代码并提取其结构;(2)从源代码构建静态调用图;(3)生成系统中所有可能执行路径的相似矩阵。我们的目标是双重的:首先,帮助开发人员理解系统的整体结构。其次,为进一步的研究提供一个跳板,可以利用该工具在软件搜索和相似度检测应用中。例如,将执行路径集群到系统的逻辑工作流中可以应用于自动化特定的软件任务。Code2graph已经成功地用于为三个流行的开源深度学习项目(TensorFlow, Keras, PyTorch)生成静态调用图和路径的相似矩阵。该工具的演示可在https://youtu.be/ecctePpcAKU上获得。
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Code2graph: Automatic Generation of Static Call Graphs for Python Source Code
A static call graph is an imperative prerequisite used in most interprocedural analyses and software comprehension tools. However, there is a lack of software tools that can automatically analyze the Python source-code and construct its static call graph. In this paper, we introduce a prototype Python tool, named code2graph, which automates the tasks of (1) analyzing the Python source-code and extracting its structure, (2) constructing static call graphs from the source code, and (3) generating a similarity matrix of all possible execution paths in the system. Our goal is twofold: First, assist the developers in understanding the overall structure of the system. Second, provide a stepping stone for further research that can utilize the tool in software searching and similarity detection applications. For example, clustering the execution paths into a logical workflow of the system would be applied to automate specific software tasks. Code2graph has been successfully used to generate static call graphs and similarity matrices of the paths for three popular open-source Deep Learning projects (TensorFlow, Keras, PyTorch). A tool demo is available at https://youtu.be/ecctePpcAKU.
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