An empirical study of real-world variability bugs detected by variability-oblivious tools
An empirical study of real-world variability bugs detected by variability-oblivious tools
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DOI：
10.1145/3338906.3338967
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发表时间：
2019-08
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通讯作者：
Austin Mordahl;Jeho Oh;Ugur Koc;Shiyi Wei;Paul Gazzillo
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作者：
Austin Mordahl;Jeho Oh;Ugur Koc;Shiyi Wei;Paul Gazzillo
许多用C开发的关键软件系统都利用了编译时的可配置性。这个软件的许多可能的配置使得通过静态分析进行bug检测变得困难。虽然已经开发了可变性感知的静态分析，但这些静态分析与最先进的静态错误检测工具之间仍然存在差距。为了收集这些工具如何执行的数据，并开发现实世界的基准，我们提出了一种方法来利用配置采样，现成的“可变性不经意”的错误检测器，和自动特征识别技术来模拟可变性感知分析。我们使用四种流行的静态分析工具在三个高度可配置的真实C项目上实例化我们的方法，获得36，061个警告，其中80%是可变性警告。我们分析了我们从这些实验中收集的警告，发现大多数结果是各种各样的变化警告，如NULL引用。然后，我们手动调查这些警告，以产生一个基准的77个确认的真正的错误（其中52个是可变性错误）的可变性感知分析的未来发展有用。
Many critical software systems developed in C utilize compile-time configurability. The many possible configurations of this software make bug detection through static analysis difficult. While variability-aware static analyses have been developed, there remains a gap between those and state-of-the-art static bug detection tools. In order to collect data on how such tools may perform and to develop real-world benchmarks, we present a way to leverage configuration sampling, off-the-shelf “variability-oblivious” bug detectors, and automatic feature identification techniques to simulate a variability-aware analysis. We instantiate our approach using four popular static analysis tools on three highly configurable, real-world C projects, obtaining 36,061 warnings, 80% of which are variability warnings. We analyze the warnings we collect from these experiments, finding that most results are variability warnings of a variety of kinds such as NULL dereference. We then manually investigate these warnings to produce a benchmark of 77 confirmed true bugs (52 of which are variability bugs) useful for future development of variability-aware analyses.