Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases
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...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-10-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/10/20/7264 |
id |
doaj-a00ac5e955a44c64a54cce7447769ef5 |
---|---|
record_format |
Article |
spelling |
doaj-a00ac5e955a44c64a54cce7447769ef52020-11-25T03:50:45ZengMDPI AGApplied Sciences2076-34172020-10-01107264726410.3390/app10207264Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test CasesWanida Khamprapai0Cheng-Fa Tsai1Paohsi Wang2Department of Tropical Agriculture and International Cooperation, National Pingtung University of Science and Technology, Pingtung 91201, TaiwanDepartment of Management Information Systems, National Pingtung University of Science and Technology, Pingtung 91201, TaiwanDepartment of Food and Beverage Management, Cheng Shiu University, Kaohsiung 83347, TaiwanSoftware 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.https://www.mdpi.com/2076-3417/10/20/7264software testingbranch coveragegenetic algorithmmultiple-search genetic algorithmnetwork systems |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wanida Khamprapai Cheng-Fa Tsai Paohsi Wang |
spellingShingle |
Wanida Khamprapai Cheng-Fa Tsai Paohsi Wang Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases Applied Sciences software testing branch coverage genetic algorithm multiple-search genetic algorithm network systems |
author_facet |
Wanida Khamprapai Cheng-Fa Tsai Paohsi Wang |
author_sort |
Wanida Khamprapai |
title |
Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases |
title_short |
Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases |
title_full |
Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases |
title_fullStr |
Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases |
title_full_unstemmed |
Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases |
title_sort |
analyzing the performance of the multiple-searching genetic algorithm to generate test cases |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2020-10-01 |
description |
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. |
topic |
software testing branch coverage genetic algorithm multiple-search genetic algorithm network systems |
url |
https://www.mdpi.com/2076-3417/10/20/7264 |
work_keys_str_mv |
AT wanidakhamprapai analyzingtheperformanceofthemultiplesearchinggeneticalgorithmtogeneratetestcases AT chengfatsai analyzingtheperformanceofthemultiplesearchinggeneticalgorithmtogeneratetestcases AT paohsiwang analyzingtheperformanceofthemultiplesearchinggeneticalgorithmtogeneratetestcases |
_version_ |
1724490805050081280 |