Performance of Enhanced Multiple-Searching Genetic Algorithm for Test Case Generation in Software Testing

Wanida Khamprapai, Cheng-Fa Tsai, Paohsi Wang and Chi-En Tsai
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Wanida Khamprapai: Department of Tropical Agriculture and International Cooperation, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
Cheng-Fa Tsai: Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
Paohsi Wang: Department of Food and Beverage Management, Cheng Shiu University, Kaohsiung 83347, Taiwan
Chi-En Tsai: Department of Multimedia Business Unit II, Realtek Semiconductor Corporation, Hsinchu 30076, Taiwan

Mathematics, 2021, vol. 9, issue 15, 1-17

Abstract: Test case generation is an important process in software testing. However, manual generation of test cases is a time-consuming process. Automation can considerably reduce the time required to create adequate test cases for software testing. Genetic algorithms (GAs) are considered to be effective in this regard. The multiple-searching genetic algorithm (MSGA) uses a modified version of the GA to solve the multicast routing problem in network systems. MSGA can be improved to make it suitable for generating test cases. In this paper, a new algorithm called the enhanced multiple-searching genetic algorithm (EMSGA), which involves a few additional processes for selecting the best chromosomes in the GA process, is proposed. The performance of EMSGA was evaluated through comparison with seven different search-based techniques, including random search. All algorithms were implemented in EvoSuite, which is a tool for automatic generation of test cases. The experimental results showed that EMSGA increased the efficiency of testing when compared with conventional algorithms and could detect more faults. Because of its superior performance compared with that of existing algorithms, EMSGA can enable seamless automation of software testing, thereby facilitating the development of different software packages.

Keywords: search-based test case generation; genetic algorithm; branch coverage; object-oriented (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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