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Clustering static analysis defect reports to reduce maintenance costs

机译:聚类静态分析缺陷报告以降低维护成本

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Static analysis tools facilitate software maintenance by automatically identifying bugs in source code. However, for large systems, these tools often produce an overwhelming number of defect reports. Many of these defect reports are conceptually similar, but addressing each report separately costs developer effort and increases the maintenance burden. We propose to automatically cluster machine-generated defect reports so that similar bugs can be triaged and potentially fixed in aggregate. Our approach leverages both syntactic and structural information available in static bug reports to accurately cluster related reports, thus expediting the maintenance process. We evaluate our technique using 8,948 defect reports produced by the Coverity Static Analysis and FindBugs tools in both C and Java programs totaling over 14 million lines of code. We find that humans overwhelmingly agree that clusters of defect reports produced by our tool could be handled aggregately, thus reducing developer maintenance effort. Additionally, we show that our tool is not only capable of perfectly accurate clusters, but can also significantly reduce the number of defect reports that have to be manually examined by developers. For instance, at a level of 90% accuracy, our technique can reduce the number of individually inspected defect reports by 21.33% while other multi-language tools fail to obtain more than a 2% reduction.
机译:静态分析工具通过自动识别源代码中的错误来促进软件维护。但是,对于大型系统,这些工具通常会产生压倒性的缺陷报告数。这些缺陷报告中的许多报告在概念上类似,但各报告分别为开发商努力提高并提高维护负担。我们建议自动群集机器生成的缺陷报告,以便可以在聚合中进行类似的错误和可能固定类似的错误。我们的方法利用了静态错误报告中提供的句法和结构信息,以准确纳入相关的报告,从而加快维护过程。我们使用8,948个缺陷报告评估我们的技术,通过COMEDITY静态分析和C和Java程序中的FindBugs工具,总计超过1400万行代码。我们发现人类压倒性地同意,我们的工具生产的缺陷报告集群可以聚合处理,从而减少开发人员维护工作。此外,我们表明我们的工具不仅能够完全准确的集群,而且还可以显着减少开发人员手动检查的缺陷报告的数量。例如,以90%的精度为90%,我们的技术可以减少单独检查的缺陷报告的数量21.33%,而其他多语言工具无法获得2%以上。

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