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.
Baah, G. K., Podgurski, A., & Harrold, M. J. (2010). Causal inference for statistical fault localization. In Proceedings of the 19th international symposium on Software testing and analysis - ISSTA ’10 (p. 73). https://doi.org/10.1145/1831708.1831717