PORA: Proportion-Oriented Randomized Algorithm for Test Case Prioritization

Abstract

Effective testing is essential for assuring software quality. While regression testing is time-consuming, the fault detection capability may be compromised if some test cases are discarded. Test case prioritization is a viable solution. To the best of our knowledge, the most effective test case prioritization approach is still the additional greedy algorithm, and existing search-based algorithms have been shown to be visually less effective than the former algorithms in previous empirical studies. This paper proposes a novel Proportion-Oriented Randomized Algorithm (PORA) for test case prioritization. PORA guides test case prioritization by optimizing the distance between the prioritized test suite and a hierarchy of distributions of test input data. Our experiment shows that PORA test case prioritization techniques are as effective as, if not more effective than, the total greedy, additional greedy, and ART techniques, which use code coverage information. Moreover, the experiment shows that PORA techniques are more stable in effectiveness than the others.