AN IMPROVED GENETIC ALGORITHM FOR TEST DATA GENERATION FOR SIMULINK MODELS

Hanh, LTM; Binh, NT; Tung, KT

AN IMPROVED GENETIC ALGORITHM FOR TEST DATA GENERATION FOR SIMULINK MODELS

Hanh, LTM Binh, NT Tung, KT

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Publisher:: Publishing House for Science and Technology, Vietnam Academy of Science and Technology (Publications)
Publication Type:: Journal Article
Citation:: Journal of Computer Science and Cybernetics, 2017, 33, (1)
Issue Date:: 2017-12-07

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Field	Value	Language
dc.contributor.author	Hanh, LTM
dc.contributor.author	Binh, NT
dc.contributor.author	Tung, KT
dc.date.accessioned	2022-09-01T02:47:11Z
dc.date.available	2022-09-01T02:47:11Z
dc.date.issued	2017-12-07
dc.identifier.citation	Journal of Computer Science and Cybernetics, 2017, 33, (1)
dc.identifier.issn	1813-9663
dc.identifier.issn	1813-9663
dc.identifier.uri	http://hdl.handle.net/10453/161162
dc.description.abstract	Mutation testing is a powerful and effective software testing technique to assess the quality of test suites. Although many research works have been done in the field of search-based testing, automatic test data generation based on the mutation analysis method is not straightforward. In this paper, an Improved Genetic Algorithm (IGA) is proposed to increase the quality of test data based on mutation coverage criterion. This algorithm involves some modifications of genetic operators and the employment of memory mechanism to enhance its effectiveness. The proposed approach is implemented to generate test data for Simulink models. The obtained results indicated that IGA outperformed the conventional genetic algorithm in terms of the quality of test sets, and the execution time.
dc.language	en
dc.publisher	Publishing House for Science and Technology, Vietnam Academy of Science and Technology (Publications)
dc.relation.ispartof	Journal of Computer Science and Cybernetics
dc.relation.isbasedon	10.15625/1813-9663/33/1/9315
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	AN IMPROVED GENETIC ALGORITHM FOR TEST DATA GENERATION FOR SIMULINK MODELS
dc.type	Journal Article
utslib.citation.volume	33
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-09-01T02:47:09Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	33
utslib.citation.issue	1

Abstract:

Mutation testing is a powerful and effective software testing technique to assess the quality of test suites. Although many research works have been done in the field of search-based testing, automatic test data generation based on the mutation analysis method is not straightforward. In this paper, an Improved Genetic Algorithm (IGA) is proposed to increase the quality of test data based on mutation coverage criterion. This algorithm involves some modifications of genetic operators and the employment of memory mechanism to enhance its effectiveness. The proposed approach is implemented to generate test data for Simulink models. The obtained results indicated that IGA outperformed the conventional genetic algorithm in terms of the quality of test sets, and the execution time.

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http://hdl.handle.net/10453/161162