Test Case Prioritizatio Based on Dual Population Genetic Algorithm

• 1、Department of Computer and Information Technology, University of BeiJingJiaoTong, BeiJing 100044

Abstract：At any stage of the software life cycle, as long as the software is modified and iterated, regression testing will be triggered to ensure that new changes will not hinder the behavior of unchanged parts. Prioritization of test cases is one of the techniques to improve the efficiency of regression testing, and search-based algorithms can improve the efficiency and effectiveness of sorting. Among the search-based prioritization algorithms for test cases, genetic algorithms have received extensive attention from researchers, but traditional genetic algorithms have the problems of premature convergence due to lack of diversity in the population and slow convergence in the later stages of evolution. Therefore, this paper introduces the dual population genetic algorithm into the test case sorting to solve the premature convergence problem, and uses the Boltmann selection method to realize the adaptive selection pressure to solve the problem of slow convergence in the later stage. Improved coverage algorithms to guide the search process. Finally, a comparative verification is carried out on the dataset Defects4j with real faults. The results show that the algorithm in this paper is superior to the traditional genetic algorithm in terms of average fault detection rate (APFD).

Keywords： computer software test case prioritization dual population genetic algorithm adaptive selection pressure