Mitigating the Dependence Confounding Effect for Effective Predicate-Based Statistical Fault Localization

The recent studies indicate that predicate-based statistical fault localization suffered from the control dependence confounding effect and the failure flow confounding effect, which decrease the measurement accuracy of fault localization. However, the extent of the potentially confounding effect of data dependence is uncertain. This paper presents a novel approach that accounts for the effects of program dependences to mitigate the confounding effect during statistical predicate-based fault localization. First, we present a variable type-based predicate designation technique to improve the ability of fault-relevant predicate identification. Then, we conduct dependence analysis to examine the extent of the potentially confounding effect of data dependence in fault localization. Finally, we propose a linear regression-based method to mitigate both the data dependence confounding effect and the control dependence confounding effect. Using the open-source software systems, we find that the fault-relevant predicate can be identified effectively by the proposed predicate design technique, and the effectiveness of fault localization can be significantly improved after mitigating the dependence confounding effect.