How well does test case prioritization integrate with statistical fault localization?
Information and Software Technology
Linking software testing results with a machine learning approach
Engineering Applications of Artificial Intelligence
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In order to improve the efficiency of regression testing, many test selection techniques have been proposed to extract a small subset from a huge test suite, which can approximate the fault detection capability of the original test suite for the modified code. This paper presents a new regression test selection technique by clustering the execution profiles of modification-traversing test cases. Cluster analysis can group program executions that have similar features, so that program behaviors can be well understood and test cases can be selected in a proper way to reduce the test suite effectively. An experiment with some real programs is designed and implemented. The experiment results show that our approach can produce a smaller test suite with most fault-revealing test cases in comparison with existing selection techniques.