Mitigating the confounding effects of program dependences for effective fault localization
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Software fault localization using feature selection
Proceedings of the International Workshop on Machine Learning Technologies in Software Engineering
Search-based fault localization
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Mining succinct predicated bug signatures
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
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In statistics and data mining communities, there have been many measures proposed to gauge the strength of association between two variables of interest, such as odds ratio, confidence, Yule-Y, Yule-Q, Kappa, and gini index. These association measures have been used in various domains, for example, to evaluate whether a particular medical practice is associated positively to a cure of a disease or whether a particular marketing strategy is associated positively to an increase in revenue, etc. This paper models the problem of locating faults as association between the execution or non-execution of particular program elements with failures. There have been special measures, termed as suspiciousness measures, proposed for the task. Two state-of-the-art measures are Tarantula and Ochiai, which are different from many other statistical measures. To the best of our knowledge, there is no study that comprehensively investigates the effectiveness of various association measures in localizing faults. This paper fills in the gap by evaluating 20 well-known association measures and compares their effectiveness in fault localization tasks with Tarantula and Ochiai. Evaluation on the Siemens programs show that a number of association measures perform statistically comparable as Tarantula and Ochiai.