A CLUSTERING-BASED STRATEGY TO IDENTIFY COINCIDENTAL CORRECTNESS IN FAULT LOCALIZATION

Abstract

Coverage-based fault localization techniques leverage the coverage information to identify the faulty elements of a program. However, these techniques can be adversely affected by coincidental correctness, which occurs when the defect is executed but no failure is revealed. In this paper, we propose a clustering-based strategy to identify coincidental correctness in fault localization. The insight behind this strategy is that tests in the same cluster have similar behaviors. Thus a passed test in a cluster with many failed tests is highly possible to be coincidentally correct because it has the potential to execute the faulty elements as those failed ones do. We evaluated this technique from two aspects: the ability to identify coincidental correctness and the effectiveness to improve fault localization. The experimental results show that our strategy can alleviate the coincidental correctness problem and improve the effectiveness of fault localization.