A Passed Test Case Cluster Method to Improve Fault Localization

Abstract

Coverage-Based Fault Localization (CBFL) techniques are based on the conjecture that the program elements executed by more failed test cases and less passed test cases have more chance to be faulty. Coincidental Correct (CC) test case is one of the negative impacts on CBFL, for the reason that the CC test cases execute the faulty element but not propagate the faulty status to the final output. To alleviate the negative impact of CC test cases, in this paper, we propose a cluster-based technique to identify CC test cases from the passed test suite. To evaluate the effectiveness of our method, we conduct empirical studies on 102 versions from six programs. The experimental results show that, when using our method, it can accurately recognize CC test cases, where the precision and recall rate are both higher than 85%. A further study shows that, after removing identified CC test cases, the fault localization accuracy of SBFL can be improved apparently.