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2010
ACM

Causal inference for statistical fault localization

10 years 3 months ago
Causal inference for statistical fault localization
This paper investigates the application of causal inference methodology for observational studies to software fault localization based on test outcomes and profiles. This methodology combines statistical techniques for counterfactual inference 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 estimating 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 relationships to our causal estimator. Finally, the paper presents empirical results demonstrating that our model significantly improves the effectiveness of fault localization. Categories and Subject Descriptors: D.2.5 [Software Engineering]: Testing and Debugging—Diagnostics, Monitors General Terms: Algorithms, Experimentation
George K. Baah, Andy Podgurski, Mary Jean Harrold
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2010
Where ISSTA
Authors George K. Baah, Andy Podgurski, Mary Jean Harrold
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