Causal inference for statistical fault localization

  • Baah G
  • Podgurski A
  • Harrold M
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Abstract

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.

Author-supplied keywords

  • causal inference
  • debugging
  • fault
  • localization
  • potential outcome model
  • program analysis

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Authors

  • George K. Baah

  • Andy Podgurski

  • Mary Jean Harrold

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