Optimal Model of Software Testing Path Selection Based on Genetic Algorithm and Its Evolutionary Solution

Abstract

In software testing, the selection of test data is a difficult problem in structural testing. Whether the test data is appropriate or not is directly related to whether the error can be expected to be detected. In the process of software testing, the generation of test data is not only the core problem but also the key and difficulty of software testing. Because of the huge number of test cases and low test efficiency, a powerful optimization algorithm is needed to optimize the initial test cases. As a robust search method, genetic algorithm shows unique advantages and high efficiency in solving high-complexity problems such as large space, multipeak, nonlinear, and global optimization. Based on the application of genetic algorithm, this paper analyzes the optimization path by classifying and calculating the objective function and introducing NSGA-II algorithm, measures the distance between each branch on the processing path sample set, and sorts the path set to obtain the optimal solution. On the basis of the designed model, the experimental results show that the error control rate of the model is 89.4%. Moreover, because of the superiority of NSGA-II algorithm, the probability of comprehensive cross mutation is increased by 56.7%.