An empirical study on clustering approach combining fault prediction for test case prioritization

Using Clustering algorithm to improve the effectiveness of test case prioritization has been well recognized by many researchers. Software fault prediction has been one of the active parts of software engineering, but to date, there are few test cases prioritization technique using fault prediction. We conjecture that if the code has a fault-proneness, the test cases covering the code will find fault with higher probability. In addition, most of the existing test cases prioritization techniques using clustering algorithm don't consider the number of clusters. Thus, in this paper, we design a test case prioritization based on clustering approach combining fault prediction. We consider the method to obtain the best number of clusters and the clustering prioritization based on the results of fault prediction. To investigate the effectiveness of our approach, we perform an empirical study using an object which contains test cases and faults. The experiment results indicate that our techniques can improve the effectiveness of test case prioritization.