2 research outputs found
Multi-Objective ANT Lion Optimization Algorithm Based Mutant Test CaseSelection for Regression Testing
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 willsolve 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
Multi-Objective ANT Lion Optimization Algorithm Based Mutant Test Case Selection for Regression Testing
582-592The 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