关键词: 黑猩猩优化算法, LHS序列, 正余弦扰动因子, 软件测试, 测试数据生成

Abstract: The key of automatically generating test data is whether it can generate data with high coverage and strong error-correcting ability. In order to solve the problems of low test data generation efficiency and low convergence precision of chimp optimization algorithm, a chimp optimization algorithm based on sine-cosine perturbation strategy（SC-ChOA） is proposed. Firstly, the Latin hypercube strategy is used to initialize the population and enhance the diversity of the population. Secondly, the nonlinear decay convergence factor is introduced to balance the global and local exploration ability of the algorithm, to avoid the algorithm stagnation caused by the local range search of the group. In addition, the test function is compared with standard chimp optimization algorithm and common genetic algorithm to verify the effectiveness of the algorithm. Finally, the improved algorithm is applied to test data generation, in order to optimize the test data, a branch function is inserted into the pile to establish the fitness function. In order to verify the effectiveness of the improved algorithm in test data generation, several benchmark programs are used to compare the algorithms, the results show that SC-ChOA has obvious advantages in the coverage, average iteration times and running time of test data generation.

Key words: chimp optimization algorithm, LHS sequence, sine-cosine perturbation factor, software testing, test data generation