Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/57976
Title: Multi-Objective ANT Lion Optimization Algorithm Based Mutant Test Case Selection for Regression Testing
Authors: Tripathi, Aprna
Srivastava, Shilpa
Mittal, Himani
Sinha, Shivaji
Yadav, Vikash
Keywords: Genetic algorithm;Matlab;Mutant test case;Regression testing;Software testing
Issue Date: Jul-2021
Publisher: NIScPR-CSIR, India
Abstract: The 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.
Page(s): 582-592
URI: http://nopr.niscair.res.in/handle/123456789/57976
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.80(07) [July 2021]

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