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False positive elimination in suspected code fault automatic confirmation

机译:误码排除可疑代码故障自动确认

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Since code static analysis can not simultaneously maintain the reliability and completeness, the analysis result has a large number of false positives. Therefore, it is great significance to eliminate the false positives. In this paper, based on the path-sensitive and demand-drive, an approach for eliminating false positives in the initial results of static analysis, and thus confirming part of suspected faults is presented. Our approach employs two main techniques, demand-driven based local program analysis and searching correlation procedures of defect feature, which can not only avoid an exhaustive program exploration, but also undertake detailed path analysis on the correlation procedures that improves the accuracy of analysis. In order to illustrate and verify our approach, an experimental study is performed on the five open source C projects. The results show that, compared with state-of-the-art approaches, our approach takes less time and can eliminate more false positives.
机译:由于代码静态分析不能同时保持可靠性和完整性,因此分析结果存在大量误报。因此,消除误报具有重要意义。本文基于路径敏感和需求驱动,提出了一种消除静态分析初始结果中的误报,从而确认部分可疑故障的方法。我们的方法采用两种主要技术,即基于需求驱动的本地程序分析和搜索缺陷特征的关联过程,这不仅可以避免详尽的程序探索,而且可以对关联过程进行详细的路径分析,从而提高了分析的准确性。为了说明和验证我们的方法,对五个开源C项目进行了实验研究。结果表明,与最新方法相比,我们的方法花费的时间更少,并且可以消除更多的误报。

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