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Multi-Objective ANT Lion Optimization Algorithm Based Mutant Test Case Selection for Regression Testing

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Title Multi-Objective ANT Lion Optimization Algorithm Based Mutant Test Case Selection for Regression Testing
 
Creator Tripathi, Aprna
Srivastava, Shilpa
Mittal, Himani
Sinha, Shivaji
Yadav, Vikash
 
Subject Genetic algorithm
Matlab
Mutant test case
Regression testing
Software testing
 
Description 582-592
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.
 
Date 2021-09-01T09:45:06Z
2021-09-01T09:45:06Z
2021-07
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/57976
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.80(07) [July 2021]