ENRE: A Tool Framework for Extensible eNtity Relation Extraction

Wuxia Jin, Yuanfang Cai, R. Kazman, Q. Zheng, Di Cui, Ting Liu
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引用次数: 12

Abstract

Understanding the dependencies among code entities is fundamental to many software analysis tools and techniques. However, with the emergence of new programming languages and paradigms, the increasingly common practice of writing systems in multiple languages, and the increasing popularity of dynamic languages, no existing framework can reliably extract this information. That is, no tools exist to accurately extract dependencies from systems written in multiple and dynamic languages. To address this problem, we have designed and implemented the Extensible eNtity Relation Extraction (ENRE) framework. ENRE supports the extraction of entities and their dependencies from systems written in multiple languages, enables the customization of dependencies of interest to the user, and makes implicit dependencies explicit. To demonstrate feasibility of this framework, we developed two ENRE instances for analyzing Python and Golang programs. Our experiments on 12 Python and Golang projects demonstrated the effectiveness and flexibility of ENRE. By comparing with a commercial static analysis tool, we show that we can extract dependencies from Golang programs which are not supported by existing tools and we can reveal implicit dependencies in Python. (Demo Video: https://youtu.be/BfXp5bb1yqc)
ENRE:可扩展实体关系抽取的工具框架
理解代码实体之间的依赖关系是许多软件分析工具和技术的基础。然而,随着新的编程语言和范式的出现,用多种语言编写系统的日益普遍的做法,以及动态语言的日益普及,没有现有的框架可以可靠地提取这些信息。也就是说,没有工具能够准确地从用多种动态语言编写的系统中提取依赖关系。为了解决这个问题,我们设计并实现了可扩展实体关系提取(ENRE)框架。ENRE支持从用多种语言编写的系统中提取实体及其依赖关系,支持自定义用户感兴趣的依赖关系,并使隐式依赖关系显式显示。为了演示这个框架的可行性,我们开发了两个ENRE实例来分析Python和Golang程序。我们在12个Python和Golang项目上的实验证明了ENRE的有效性和灵活性。通过与商业静态分析工具的比较,我们展示了我们可以从现有工具不支持的Golang程序中提取依赖关系,并且可以揭示Python中的隐式依赖关系。(演示视频:https://youtu.be/BfXp5bb1yqc)
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