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Failures with different root causes can disturb multi-fault localizationsignificantly, therefore, dividing failures into distinct groups according tothe responsible faults is highly important. In such a failure indexing task,the crux lies in the failure proximity, which involves two points, i.e., how toeffectively represent failures (e.g., extract the signature of failures) andhow to properly measure the distance between the proxies for those failures.Existing studies have proposed a variety of failure proximities. The prevalentof them extract signatures of failures from execution coverage orsuspiciousness ranking lists, and accordingly employ the Euclid or the Kendalltau distances. However, such strategies may not properly reflect the essentialcharacteristics of failures, thus resulting in unsatisfactory effectiveness. Inthis paper, we propose a new failure proximity, namely, program variable-basedfailure proximity, and based on which present a novel failure indexingapproach. Specifically, the proposed approach utilizes the run-time values ofprogram variables to represent failures, and designs a set of rules to measurethe similarity between them. Experimental results demonstrate thecompetitiveness of the proposed approach: it can achieve 44.12% and 27.59%improvements in faults number estimation, as well as 47.30% and 26.93%improvements in clustering effectiveness, compared with the state-of-the-arttechnique in this field, in simulated and real-world environments,respectively.

A novel failure indexing approach with run-time values of program variables