Of Massive Static Analysis Data

A. Delaitre, Vadim Okun, E. Fong
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引用次数: 5

Abstract

The Software Assurance Metrics and Tool Evaluation (SAMATE) project at the National Institute of Standards and Technology (NIST) has organized four Static Analysis Tool Expositions (SATE). SATE is designed to advance research in static analysis tools that find security-relevant defects in source code. Briefly, participating tool makers run their tools on a set of programs. Researchers led by NIST analyze the tool outputs. The results and experiences are reported at a workshop. These expositions have accumulated large amounts of data. This collection allowed for the development and validation of practical metrics in regard to static analysis tool effectiveness and independence. In this paper, we discuss the role of the data in determining which metrics can be derived. Specifically, we detail the three characteristics test data should exhibit and explain why the data we use express each combination of two out of these three properties.
海量静态分析数据
美国国家标准与技术研究所(NIST)的软件保证度量和工具评估(SAMATE)项目已经组织了四次静态分析工具展览会(SATE)。SATE旨在推进静态分析工具的研究,这些工具可以发现源代码中与安全相关的缺陷。简而言之,参与的工具制造商在一组程序上运行他们的工具。由NIST领导的研究人员分析了工具输出。在一个研讨会上报告了结果和经验。这些博览会积累了大量的数据。这个集合允许开发和验证关于静态分析工具有效性和独立性的实用度量。在本文中,我们讨论了数据在确定可以导出哪些度量方面的作用。具体来说,我们详细说明了测试数据应该展示的三个特征,并解释了为什么我们使用的数据表达了这三个属性中的两个的每种组合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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