Computer Science ›› 2017, Vol. 44 ›› Issue (6): 177-181.doi: 10.11896/j.issn.1002-137X.2017.06.030

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Test Case Generation Method Based on Adaptive Particle Swarm Optimization

BAO Xiao-an, YANG Ya-juan, ZHANG Na, LIN Qing-xia and YU Cheng-hai   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Obtaining minimum coverage array is one of the key issues in the combination test.Particle swarm optimization(PSO),as one of the evolutionary search based methods,can obtain the smallest covering arrays,but its performance is significantly impacted by the parameters.To solve this problem,we combined one-test-at-a-time strategy and particle swarm optimization and proposed an adaptive particle swarm optimization algorithm.Based on the quality of the particles in the population,the strategy adaptively adjusts inertia weights which makes it have stronger ability of application.In order to further improve the performance of the algorithm,we constructed a priority measure function which is used to measure the weight of each combination,and we preferred to select a combination which has the highest weight to generate a single test case.Finally the paper implemented the algorithm by programming,and compared this approach with the original particle swarm optimization algorithm in test suite size and generation time.The results show the competitiveness of this approach.

Key words: Combination testing,Covering array generation,Particle swarm optimization,Adaptive algorithm

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