This paper investigates the application of causal inference methodology for observational studies to software fault lo- calization based on test outcomes and profiles. This method- ology combines statistical techniques for counterfactual in- ference with causal graphical models to obtain causal-effect estimates that are not subject to severe confounding bias. The methodology applies Pearl’s Back-Door Criterion to program dependence graphs to justify a linear model for esti- mating the causal effect of covering a given statement on the occurrence of failures. The paper also presents the analysis of several proposed-fault localization metrics and their rela- tionships to our causal estimator. Finally, the paper presents empirical results demonstrating that our model significantly improves the effectiveness of fault localization.
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