Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases

• Main Authors: Wanida Khamprapai, Cheng-Fa Tsai, Paohsi Wang
• Format: Article
• Language: English
• Published: MDPI AG 2020-10-01
• Series: Applied Sciences
• Subjects: software testing; branch coverage; genetic algorithm; multiple-search genetic algorithm; network systems
• Online Access: https://www.mdpi.com/2076-3417/10/20/7264

Software testing using traditional genetic algorithms (GAs) minimizes the required number of test cases and reduces the execution time. Currently, GAs are adapted to enhance performance when finding optimal solutions. The multiple-searching genetic algorithm (MSGA) has improved upon current GAs and is used to find the optimal multicast routing in network systems. This paper presents an analysis of the optimization of test case generations using the MSGA by defining suitable values of MSGA parameters, including population size, crossover operator, and mutation operator. Moreover, in this study, we compare the performance of the MSGA with a traditional GA and hybrid GA (HGA). The experimental results demonstrate that MSGA reaches the maximum executed branch statements in the lowest execution time and the smallest number of test cases compared to the GA and HGA.