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Statistical fault localization (SFL) techniques use execution profiles andsuccess/failure information from software executions, in conjunction withstatistical inference, to automatically score program elements based on howlikely they are to be faulty. SFL techniques typically employ one type ofprofile data: either coverage data, predicate outcomes, or variable values.Most SFL techniques actually measure correlation, not causation, betweenprofile values and success/failure, and so they are subject to confounding biasthat distorts the scores they produce. This paper presents a new SFL technique,named \emph{UniVal}, that uses causal inference techniques and machine learningto integrate information about both predicate outcomes and variable values tomore accurately estimate the true failure-causing effect of program statements.\emph{UniVal} was empirically compared to several coverage-based,predicate-based, and value-based SFL techniques on 800 program versions withreal faults.

Improving Fault Localization by Integrating Value and Predicate Based Causal Inference Techniques