Repetitive mutations in genetic algorithm for software test data generations

Generating test data is the most important part of dynamic software testing. One of the white box testing techniques is path coverage testing. Genetic Algorithm (GA) has proven to be an important method in generating test data for automatic path coverage testing. However, to satisfy path coverage te...

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Main Author:	Kadhim, Mohammed Majid
Format:	Thesis
Language:	eng eng
Published:	2022
Subjects:	T58.5-58.64 Information technology
Online Access:	https://etd.uum.edu.my/10925/1/s827647_01.pdf https://etd.uum.edu.my/10925/2/s827647_02.pdf
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id	my-uum-etd.10925
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spelling	my-uum-etd.109252024-01-23T07:15:06Z Repetitive mutations in genetic algorithm for software test data generations 2022 Kadhim, Mohammed Majid Che Ani, Zhamri Omar, Mazni Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduates School of Arts & Sciences T58.5-58.64 Information technology Generating test data is the most important part of dynamic software testing. One of the white box testing techniques is path coverage testing. Genetic Algorithm (GA) has proven to be an important method in generating test data for automatic path coverage testing. However, to satisfy path coverage testing, GA’s operation of a single mutation generates test data that covers the same path in a single generation, hence resulting in path coverage duplication, which negatively increases the number of iterations. Therefore, this study proposes a repetitive mutation for GA in order to eliminate path coverage duplication and reduce the number of iterations for test data generations in path coverage testing. The study was conducted in three phases. First, the limitations of existing mutation techniques used in GA to generate test data for path coverage testing were analysed. Then, a repetitive mutation technique for GA was designed and implemented in a numerical simulation using C++ language. Finally, the evaluation phase that compares the outcome of the proposed technique against existing studies in terms of the number of iterations for test data generations. The findings show that the proposed repetitive mutation technique outperformed the single mutation technique by reducing the number of iterations to more than 50 percent for test data generations. The study has revealed the importance of mutation in generating test data and how it can be harnessed to quickly guide GA in producing solutions. In addition, the proposed repetitive mutation in GA can contribute to developing an adaptive GA testing tool. 2022 Thesis https://etd.uum.edu.my/10925/ https://etd.uum.edu.my/10925/1/s827647_01.pdf text eng 2025-08-23 staffonly https://etd.uum.edu.my/10925/2/s827647_02.pdf text eng public other masters Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Che Ani, Zhamri Omar, Mazni
topic	T58.5-58.64 Information technology
spellingShingle	T58.5-58.64 Information technology Kadhim, Mohammed Majid Repetitive mutations in genetic algorithm for software test data generations
description	Generating test data is the most important part of dynamic software testing. One of the white box testing techniques is path coverage testing. Genetic Algorithm (GA) has proven to be an important method in generating test data for automatic path coverage testing. However, to satisfy path coverage testing, GA’s operation of a single mutation generates test data that covers the same path in a single generation, hence resulting in path coverage duplication, which negatively increases the number of iterations. Therefore, this study proposes a repetitive mutation for GA in order to eliminate path coverage duplication and reduce the number of iterations for test data generations in path coverage testing. The study was conducted in three phases. First, the limitations of existing mutation techniques used in GA to generate test data for path coverage testing were analysed. Then, a repetitive mutation technique for GA was designed and implemented in a numerical simulation using C++ language. Finally, the evaluation phase that compares the outcome of the proposed technique against existing studies in terms of the number of iterations for test data generations. The findings show that the proposed repetitive mutation technique outperformed the single mutation technique by reducing the number of iterations to more than 50 percent for test data generations. The study has revealed the importance of mutation in generating test data and how it can be harnessed to quickly guide GA in producing solutions. In addition, the proposed repetitive mutation in GA can contribute to developing an adaptive GA testing tool.
format	Thesis
qualification_name	other
qualification_level	Master's degree
author	Kadhim, Mohammed Majid
author_facet	Kadhim, Mohammed Majid
author_sort	Kadhim, Mohammed Majid
title	Repetitive mutations in genetic algorithm for software test data generations
title_short	Repetitive mutations in genetic algorithm for software test data generations
title_full	Repetitive mutations in genetic algorithm for software test data generations
title_fullStr	Repetitive mutations in genetic algorithm for software test data generations
title_full_unstemmed	Repetitive mutations in genetic algorithm for software test data generations
title_sort	repetitive mutations in genetic algorithm for software test data generations
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2022
url	https://etd.uum.edu.my/10925/1/s827647_01.pdf https://etd.uum.edu.my/10925/2/s827647_02.pdf
_version_	1794023790490943488

Repetitive mutations in genetic algorithm for software test data generations

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