Test case generation method based on particle swarm optimization algorithm

Ke Wang; Yi Zhu; Guorui Li; Junjie Wang; Ziyang Liu

doi:10.1117/12.2683538

26 June 2023 Test case generation method based on particle swarm optimization algorithm

Ke Wang, Yi Zhu, Guorui Li, Junjie Wang, Ziyang Liu

Proceedings Volume 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023); 127211L (2023) https://doi.org/10.1117/12.2683538
Event: 2023 2nd International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2023, Chengdu, China

Abstract

To address the problem of low efficiency in test case generation, an Elite Opposition-Learning Particle Swarm Optimization Based on Selection and Mutation Strategy (SM-EOLPSO) is proposed in this paper. Firstly, nonlinear decreasing inertia weight with random offset is set so that the search ability can be adaptively adjusted to the situation. Secondly, opposition-based learning is performed to enhance global detection ability and improve population diversity; meanwhile, selection and mutation operations in genetic algorithm are introduced to speed up convergence and prevent falling into local optimal solutions. Finally, the branch distance is used to construct the fitness function and evaluate test cases. Experimental results show that the algorithm is competitive in terms of the number of iterations and generation time for automatic test case generation.

Citation Download Citation

Ke Wang, Yi Zhu, Guorui Li, Junjie Wang, and Ziyang Liu "Test case generation method based on particle swarm optimization algorithm", Proc. SPIE 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 127211L (26 June 2023); https://doi.org/10.1117/12.2683538

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