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引用次数: 2

摘要

在错误的可配置系统中,由于功能之间的意外交互,与配置相关的错误仅在某些配置中导致失败。由于可配置系统的复杂性,手动定位与配置相关的故障可能非常耗时。然而,现有的自动化故障定位技术并没有考虑到与配置相关的错误的原因,这些技术的目的是在非可配置代码中定位错误。因此,他们的能力有效的配置依赖的定位是有限的。在这项工作中,我们提出了COFL,这是一种通过识别和分析可能导致错误的可配置系统故障的可疑特征交互来定位配置相关错误的新方法。我们评估了COFL在高度可配置系统中人工配置相关故障的故障定位效率。我们发现COFL显著改善了基于基线光谱的方法。使用COFL,对错误语句排序的正确性平均提高了7倍以上,搜索空间明显缩小,约为15倍。
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Configuration-Dependent Fault Localization
In a buggy configurable system, configuration-dependent bugs cause the failures in only certain configurations due to unexpected interactions among features. Manually localizing configuration-dependent faults in configurable systems could be highly time-consuming due to their complexity. However, the cause of configuration-dependent bugs is not considered by existing automated fault localization techniques, which are designed to localize bugs in non-configurable code. Thus, their capacity for efficient configuration-dependent localization is limited. In this work, we propose COFL, a novel approach to localize configuration-dependent bugs by identifying and analyzing suspicious feature interactions that potentially cause the failures in buggy configurable systems. We evaluated the efficiency of COFL in fault localization of artificial configuration-dependent faults in a highly-configurable system. We found that COFL significantly improves the baseline spectrum-based approaches. With COFL, on average, the correctness in ranking the buggy statements increases more than 7 times, and the search space is significantly narrowed down, about 15 times.
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