Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/57976
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dc.contributor.authorTripathi, Aprna-
dc.contributor.authorSrivastava, Shilpa-
dc.contributor.authorMittal, Himani-
dc.contributor.authorSinha, Shivaji-
dc.contributor.authorYadav, Vikash-
dc.date.accessioned2021-09-01T09:45:06Z-
dc.date.available2021-09-01T09:45:06Z-
dc.date.issued2021-07-
dc.identifier.issn0975-1084 (Online); 0022-4456 (Print)-
dc.identifier.urihttp://nopr.niscair.res.in/handle/123456789/57976-
dc.description582-592en_US
dc.description.abstractThe regression testing is principally carried out on modified parts of the programs. The quality of programs is the only concern of regression testing in the case of produced software. Main challenges to select mutant test cases are related to the affected classes. In software regression testing, the identification of optimal mutant test case is another challenge. In this research work, an evolutionary approach multi objective ant-lion optimization (MOALO) is proposed to identify optimal mutant test cases. The selection of mutant test cases is processed as multi objective enhancement problem and these will solve through MOALO algorithm. Optimal identification of mutant test cases is carried out by using the above algorithm which also enhances the regression testing efficiency. The proposed MOALO methods are implemented and tested using the Mat Lab software platform. On considering the populace size of 100, at that point the fitness estimation of the proposed framework, NSGA, MPSO, and GA are 3, 2.4, 1, and 0.3 respectively. The benefits and efficiencies of proposed methods are compared with random testing and existing works utilizing NSGA-II, MPSO, genetic algorithms in considerations of test effort, mutation score, fitness value, and time of execution. It is found that the execution times of MOALO, NSGA, MPSO, and GA are 2.8, 5, 6.5, and 7.8 respectively. Finally, it is observed that MOALO has higher fitness estimation with least execution time which indicates that MOALO methods provide better results in regression testing.en_US
dc.language.isoenen_US
dc.publisherNIScPR-CSIR, Indiaen_US
dc.sourceJSIR Vol.80(07) [July 2021]en_US
dc.subjectGenetic algorithmen_US
dc.subjectMatlaben_US
dc.subjectMutant test caseen_US
dc.subjectRegression testingen_US
dc.subjectSoftware testingen_US
dc.titleMulti-Objective ANT Lion Optimization Algorithm Based Mutant Test Case Selection for Regression Testingen_US
dc.typeArticleen_US
Appears in Collections:JSIR Vol.80(07) [July 2021]

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