Evolutionary test case generation with improved genetic algorithm

Abstract

Complex software systems often possess complex logical structures, and automatic test case generation may be difficult to fully cover complex business logic. To address this issue, an evolutionary test case generation model with radial basis function neural network and improved genetic algorithm is proposed. Firstly, traditional genetic algorithm is improved, and then evolutionary test case generation model is established. The results show that the average execution time of the improved genetic algorithm proposed in the study is 50.878 s, which is approximately 0.18 s faster than that of the improved adaptive genetic algorithm. The average number of iterations for the improved genetic algorithm in triangle classification is 95.27, and in date calculation, it is 74.51. These figures are 14.11 and 18.36 lower than those of the improved adaptive genetic algorithm, respectively. The proposed model achieves 100% average and maximum coverage in five classic benchmark programs, and significantly reduces the number of iterations compared to traditional genetic algorithms. To sum up, model developed in the study has a good application effect in automatic generation of test cases and promoting computer software applications.

Keywords

Genetic algorithm test case radial basis function neural network software test

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