Abstract: Spectrum-based fault localization(SBFL) techniques have been widely studied to help developers quickly find the position of the fault,so as to reduce the cost of program debugging.However,there is a special test case in the test suites that executes the fault statement but outputs the expected result,and this test case is called coincidental correct(CC) test case.CC test case can negatively effect the performance of SBFL fault localization.In order to mitigate the negative impact of CC test case and enhance the performance of SBFL technique,this paper proposes CC test cases identification via machine learning approach(CCIML).CCIML approach utilizes features extracted from the SBFL suspiciousness formula and program static features to identify CC test cases,thus improving the fault localization accuracy of SBFL technique.To evaluate the performance of CCIML approach,experiments are carried out on the Defects4J dataset.The experimental results show that the average recall,precision,and F₁ score of the CCIML approach for identifying CC test cases are 63.89%,70.16%,and 50.64%,respectively,better than the baselines.In addition,after processing the CC test cases identified by the CCIML approach using the cleaning and relabeling strategies,the fault localization performance obtained is also better than the comparison baselines.Under the cleaning and relabe-ling strategy,the number of faulty statements ranked first in suspicion value are 328 and 312,respectively.Compared to the fuzzy weighted K-nearest neighbor(FW-KNN) approach,the fault localization accuracy is improved by 124.66% and 235.48%.

Key words: Software debugging, Fault localization, Machine learning, Coincidental correct test case, Feature extraction