A Novel Fitness function of metaheuristic algorithms for test data generation for simulink models based on mutation analysis

Hanh, LTM; Binh, NT; Tung, KT

A Novel Fitness function of metaheuristic algorithms for test data generation for simulink models based on mutation analysis

Hanh, LTM Binh, NT Tung, KT

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Publisher:: ELSEVIER SCIENCE INC
Publication Type:: Journal Article
Citation:: Journal of Systems and Software, 2016, 120, pp. 17-30
Issue Date:: 2016-10-01

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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	2022-08-15T03:08:40Z
dc.date.available	2022-08-15T03:08:40Z
dc.date.issued	2016-10-01
dc.identifier.citation	Journal of Systems and Software, 2016, 120, pp. 17-30
dc.identifier.issn	0164-1212
dc.identifier.issn	1873-1228
dc.identifier.uri	http://hdl.handle.net/10453/160186
dc.description.abstract	Testing is one of the crucial activities to assure the software quality. The main objective of testing is to generate test data uncovering faults in software modules. There are a variety of testing techniques in which mutation testing is a popular approach to generate test sets and evaluate their fault detection ability. Simulink is an environment widely used in industry to design and simulate critical systems. Testing such a system at the design phase could help to detect faults earlier. This study aims to propose a novel fitness function of metaheuristic algorithms to generate test data based on the mutation technique for the Simulink models. The fitness function is designed by analyzing each mutation operator and the features of blocks in the Simulink environment in order to guide the search process to reach the test data killing mutants more easily. Then, this fitness function is used in the multi-parent crossover genetic algorithm to generate test sets. The obtained results indicated that the mutation score has been significantly improved for all models when using the novel fitness function. In addition, each stubborn mutant was killed with a lower number of test data evaluations in comparison with the work of other authors.
dc.language	English
dc.publisher	ELSEVIER SCIENCE INC
dc.relation.ispartof	Journal of Systems and Software
dc.relation.isbasedon	10.1016/j.jss.2016.07.001
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0804 Data Format, 0806 Information Systems
dc.subject.classification	Software Engineering
dc.title	A Novel Fitness function of metaheuristic algorithms for test data generation for simulink models based on mutation analysis
dc.type	Journal Article
utslib.citation.volume	120
utslib.for	0803 Computer Software
utslib.for	0804 Data Format
utslib.for	0806 Information Systems
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	*
dc.date.updated	2022-08-15T03:08:39Z
pubs.publication-status	Published
pubs.volume	120

Abstract:

Testing is one of the crucial activities to assure the software quality. The main objective of testing is to generate test data uncovering faults in software modules. There are a variety of testing techniques in which mutation testing is a popular approach to generate test sets and evaluate their fault detection ability. Simulink is an environment widely used in industry to design and simulate critical systems. Testing such a system at the design phase could help to detect faults earlier. This study aims to propose a novel fitness function of metaheuristic algorithms to generate test data based on the mutation technique for the Simulink models. The fitness function is designed by analyzing each mutation operator and the features of blocks in the Simulink environment in order to guide the search process to reach the test data killing mutants more easily. Then, this fitness function is used in the multi-parent crossover genetic algorithm to generate test sets. The obtained results indicated that the mutation score has been significantly improved for all models when using the novel fitness function. In addition, each stubborn mutant was killed with a lower number of test data evaluations in comparison with the work of other authors.

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http://hdl.handle.net/10453/160186