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

Statistical fault localization (SFL) techniques use execution profiles and success/failure information from software executions, in conjunction with statistical inference, to automatically score program elements based on how likely they are to be faulty. SFL techniques typically employ one type of p...

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Bibliographic Details
Published inProceedings / International Conference on Software Engineering pp. 649 - 660
Main Authors Kucuk, Yigit, Henderson, Tim A. D., Podgurski, Andy
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2021
Subjects
Causal Inference
Correlation
Distortion measurement
Fault Localization
Location awareness
Machine learning
Numerical models
Software
Software engineering
Software Fault Localization
Statistical Fault Localization
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Summary:Statistical fault localization (SFL) techniques use execution profiles and success/failure information from software executions, in conjunction with statistical inference, to automatically score program elements based on how likely they are to be faulty. SFL techniques typically employ one type of profile data: either coverage data, predicate outcomes, or variable values. Most SFL techniques actually measure correlation, not causation, between profile values and success/failure, and so they are subject to confounding bias that distorts the scores they produce. This paper presents a new SFL technique, named UniVal, that uses causal inference techniques and machine learning to integrate information about both predicate outcomes and variable values to more accurately estimate the true failure-causing effect of program statements. UniVal was empirically compared to several coverage-based, predicate-based, and value-based SFL techniques on 800 program versions with real faults.
ISBN:1665402962
9781665402965
ISSN:1558-1225
DOI:10.1109/ICSE43902.2021.00066