Test Oracle Prediction for Mutation Based Fault Localization

In the process of software debugging, it is very critical and difficult to identify the locations of faults in an effective and accurate manner. Mutation-based fault localization (MBFL) is one of the most effective automated fault localization techniques that have been recently proposed, and it requires the execution results (passed or failed) of test cases to locate faults. One problem preventing MBFL from becoming a practical testing technique is the large amount of human effort involved, i.e., the test oracle problem, which refers to the process of checking an original program’s output of each test case. To mitigate the impact of this problem, we use mutant coverage information and learning algorithms to predict the oracle of the test cases in this paper. Empirical results show that the proposed method can reduce 80% of the human cost required to check the test oracles and achieve almost the same fault localization accuracy as compared to the original MBFL.