Ontology based negative selection approach for mutation testing

Mutation testing is used to design new software tests and evaluate the quality of existing software tests. It works by seeding faults in the software program, which are called mutants. Test cases are executed on these mutants to determine if they are killed or remain alive. They remain alive because...

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Main Author:	Sualim, Sherolwendy
Format:	Thesis
Language:	English
Published:	2019
Subjects:	Q Science (General)
Online Access:	http://eprints.utm.my/id/eprint/98276/1/SherolwendySualimMSC2019.pdf
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spelling	my-utm-ep.982762022-11-30T04:59:12Z Ontology based negative selection approach for mutation testing 2019 Sualim, Sherolwendy Q Science (General) QA75 Electronic computers. Computer science Mutation testing is used to design new software tests and evaluate the quality of existing software tests. It works by seeding faults in the software program, which are called mutants. Test cases are executed on these mutants to determine if they are killed or remain alive. They remain alive because some of the mutants are syntactically different from the original, but are semantically the same. This makes it difficult for them to be identified by the test suites. Such mutants are called equivalent mutants. Many approaches have been developed by researchers to discover equivalent mutant but the results are not satisfactory. This research developed an ontology based negative selection algorithm (NSA), designed for anomalies detection and similar pattern recognition with two-class classification problem domains, either self (normal) or non-self (anomaly). In this research, an ontology was used to remove redundancies in test suites before undergoing detection process. During the process, NSA was used to detect the equivalent mutant among the test suites. Those who passed the condition set would be added to the equivalent coverage. The results were compared with previous works, and showed that the implementation of NSA in equivalent mutation testing had minimized local optimization problem in detector convergence (number of detectors) and time complexity (execution time). The findings had more equivalent mutants with average of 91.84% and scored higher mutation score (MS) with average of 80% for all the tested programs. Furthermore, the NSA had used a minimum number of detectors for higher detection of equivalent mutants with the average of 78% for all the tested programs. These results proved that the ontology based negative selection algorithm had achieved its goals to minimize local optimization problem. 2019 Thesis http://eprints.utm.my/id/eprint/98276/ http://eprints.utm.my/id/eprint/98276/1/SherolwendySualimMSC2019.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:144558 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Computing Faculty of Engineering - School of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	Q Science (General) Q Science (General)
spellingShingle	Q Science (General) Q Science (General) Sualim, Sherolwendy Ontology based negative selection approach for mutation testing
description	Mutation testing is used to design new software tests and evaluate the quality of existing software tests. It works by seeding faults in the software program, which are called mutants. Test cases are executed on these mutants to determine if they are killed or remain alive. They remain alive because some of the mutants are syntactically different from the original, but are semantically the same. This makes it difficult for them to be identified by the test suites. Such mutants are called equivalent mutants. Many approaches have been developed by researchers to discover equivalent mutant but the results are not satisfactory. This research developed an ontology based negative selection algorithm (NSA), designed for anomalies detection and similar pattern recognition with two-class classification problem domains, either self (normal) or non-self (anomaly). In this research, an ontology was used to remove redundancies in test suites before undergoing detection process. During the process, NSA was used to detect the equivalent mutant among the test suites. Those who passed the condition set would be added to the equivalent coverage. The results were compared with previous works, and showed that the implementation of NSA in equivalent mutation testing had minimized local optimization problem in detector convergence (number of detectors) and time complexity (execution time). The findings had more equivalent mutants with average of 91.84% and scored higher mutation score (MS) with average of 80% for all the tested programs. Furthermore, the NSA had used a minimum number of detectors for higher detection of equivalent mutants with the average of 78% for all the tested programs. These results proved that the ontology based negative selection algorithm had achieved its goals to minimize local optimization problem.
format	Thesis
qualification_level	Master's degree
author	Sualim, Sherolwendy
author_facet	Sualim, Sherolwendy
author_sort	Sualim, Sherolwendy
title	Ontology based negative selection approach for mutation testing
title_short	Ontology based negative selection approach for mutation testing
title_full	Ontology based negative selection approach for mutation testing
title_fullStr	Ontology based negative selection approach for mutation testing
title_full_unstemmed	Ontology based negative selection approach for mutation testing
title_sort	ontology based negative selection approach for mutation testing
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Computing
granting_department	Faculty of Engineering - School of Computing
publishDate	2019
url	http://eprints.utm.my/id/eprint/98276/1/SherolwendySualimMSC2019.pdf
_version_	1776100572434268160

Ontology based negative selection approach for mutation testing

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