Mitigating the confounding effects of program dependences for effective fault localization

Abstract

Dynamic program dependences are recognized as important factors in software debugging because they contribute to triggering the effects of faults and propagating the effects to a program's output. The effects of dynamic dependences also produce significant confounding bias when statistically estimating the causal effect of a statement on the occurrence of program failures, which leads to poor fault localization results. This paper presents a novel causal-inference technique for fault localization that accounts for the effects of dynamic data and control dependences and thus, significantly reduces confounding bias during fault localization. The technique employs a new dependence-based causal model together with matching of test executions based on their dynamic dependences. The paper also presents empirical results indicating that the new technique performs significantly better than existing statistical fault-localization techniques as well as our previous fault localization technique based on causal-inference methodology.