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To what extent could we detect field defects? An extended empirical study of false negatives in static bug-finding tools

机译:我们可以在多大程度上检测到现场缺陷?静态错误查找工具中的假阴性的扩展实证研究

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Software defects can cause much loss. Static bug-finding tools are designed to detect and remove software defects and believed to be effective. However, do such tools in fact help prevent actual defects that occur in the field and reported by users? If these tools had been used, would they have detected these field defects, and generated warnings that would direct programmers to fix them? To answer these questions, we perform an empirical study that investigates the effectiveness of five state-of-the-art static bug-finding tools (FindBugs, JLint, PMD, CheckStyle, and JCSC) on hundreds of reported and fixed defects extracted from three open source programs (Lucene, Rhino, and AspectJ). Our study addresses the question: To what extent could field defects be detected by state-of-the-art static bug-finding tools? Different from past studies that are concerned with the numbers of false positives produced by such tools, we address an orthogonal issue on the numbers of false negatives. We find that although many field defects could be detected by static bug-finding tools, a substantial proportion of defects could not be flagged. We also analyze the types of tool warnings that are more effective in finding field defects and characterize the types of missed defects. Furthermore, we analyze the effectiveness of the tools in finding field defects of various severities, difficulties, and types.
机译:软件缺陷可能会造成很多损失。静态错误查找工具旨在检测和消除软件缺陷,并认为是有效的。但是,这样的工具实际上是否有助于防止在现场发生并由用户报告的实际缺陷?如果使用了这些工具,它们是否会检测到这些现场缺陷,并生成警告,指导程序员进行修复?为了回答这些问题,我们进行了一项实证研究,调查了从三种方法中提取的数百种已报告和已修复的缺陷,这五种最新的静态错误查找工具(FindBugs,JLint,PMD,CheckStyle和JCSC)的有效性。开源程序(Lucene,Rhino和AspectJ)。我们的研究解决了一个问题:最新的静态错误查找工具可以在多大程度上检测出现场缺陷?与以往的研究中有关此类工具产生的假阳性数的研究不同,我们解决了关于假阴性数的正交问题。我们发现,尽管可以通过静态的缺陷查找工具检测到许多现场缺陷,但无法标记出很大比例的缺陷。我们还将分析在发现现场缺陷方面更有效的工具警告类型,并描述遗漏缺陷的类型。此外,我们分析了发现各种严重程度,困难和类型的现场缺陷的工具的有效性。

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