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<p><strong>Multi-Objective ANT Lion Optimization Algorithm Based Mutant Test Case</strong><br /><strong>Selection for Regression Testing</strong></p>

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Title Statement <p><strong>Multi-Objective ANT Lion Optimization Algorithm Based Mutant Test Case</strong><br /><strong>Selection for Regression Testing</strong></p>
 
Added Entry - Uncontrolled Name Tripathi, Aprna ; VIT Bhopal University, Madhya Pradesh, India
Srivastava, Shilpa ; Christ University, India
Mittal, Himani ; Raj Kumar Goel Institute of Technology, Ghaziabad, Uttar Pradesh, India
Sinha, Shivaji ; JSS Academy of Technical Education, Noida, Uttar Pradesh, India
Yadav, Vikash ; ABES Engineering College, Ghaziabad, Uttar Pradesh, India
 
Uncontrolled Index Term Genetic algorithm, Matlab, Mutant test case, Regression testing, Software testing
 
Summary, etc. <p style="text-align: justify;">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<br />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.</p>
 
Publication, Distribution, Etc. Journal of Scientific and Industrial Research (JSIR)
2021-09-26 12:59:49
 
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http://op.niscair.res.in/index.php/JSIR/article/view/46077
 
Data Source Entry Journal of Scientific and Industrial Research (JSIR); ##issue.vol## 80, ##issue.no## 7 (2021): Journal of Scientific and Industrial Research
 
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