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CBGA-ES⁺: A Cluster-Based Genetic Algorithm with Non-Dominated Elitist Selection for Supporting multi-objective test optimization

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2021年第1期47卷 86-107页

作者： Pradhan, Dipesh Wang, Shuai Ali, Shaukat Yue, Tao Liaaen, Marius Simula Res Lab POB 134 N-1325 Lysaker Norway Univ Oslo Problemveien 7 N-0315 Oslo Norway Testify As POB 134 N-1325 Lysaker Norway Simula Res Lab POB 134 N-1325 Lysaker Norway Cisco Syst Oslo Norway

Many real-world test optimization problems (e.g., test case prioritization) are multi-objective intrinsically and can be tackled using various multi-objective search algorithms (e.g., Non-dominated Sorting Genetic Algorithm (NSGA-II)). However, existing multi-objective search algorithms have certain randomness when selecting parent solutions for producing offspring solutions. In a worse case, suboptimal parent solutions may result in offspring solutions with bad quality, and thus affect the overall quality of the solutions in the next generation. To address such a challenge, we propose CBGA-ES+, a novel cluster-based genetic algorithm with non-dominated elitist selection to reduce the randomness when selecting the parent solutions to support multi-objective test optimization. We empirically compared CBGA-ES+ with random search and greedy (as baselines), four commonly used multi-objective search algorithms (i.e., multi-objective Cellular genetic algorithm (MOCell), NSGA-II, Pareto Archived Evolution Strategy (PAES), and Strength Pareto Evolutionary Algorithm (SPEA2)), and the predecessor of CBGA-ES+ (named CBGA-ES) using five multi-objective test optimization problems with eight subjects (two industrial, one real world, and five open source). The results showed that CBGA-ES+ managed to significantly outperform the selected search algorithms for a majority of the experiments. Moreover, for the solutions in the same search space, CBGA-ES+ managed to perform better than CBGA-ES, MOCell, NSGA-II, PAES, and SPEA2 for 2.2, 13.6, 14.5, 17.4, and 9.9 percent, respectively. Regarding the running time of the algorithm, CBGA-ES+ was faster than CBGA-ES for all the experiments.

关键词： Elitist selection multi-objective genetic algorithm multi-objective test optimization search

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CBGA-ES: A Cluster-Based Genetic Algorithm with Elitist Selection for Supporting multi-objective test optimization 10

CBGA-ES: A Cluster-Based Genetic Algorithm with Elitist Sele...

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10th IEEE International Conference on Software testing, Verification and Validation (ICSTW)

作者： Pradhan, Dipesh Wang, Shuai Ali, Shaukat Yue, Tao Liaaen, Marius Certus V&V Ctr Simula Res Lab Oslo Norway Univ Oslo Oslo Norway Cisco Syst Oslo Norway

ISBN: (纸本)9781509060313

multi-objective search algorithms (e.g., non dominated sorting genetic algorithm II (NSGA-II)) have been frequently applied to address various testing problems requiring multi-objective optimization such as test case selection. However, existing multi-objective search algorithms have certain randomness when selecting parent solutions for producing offspring solutions. In the worse case, suboptimal parent solutions may result in offspring solutions with bad quality, and thus affect the overall quality of the next generation. To address such a challenge, we propose a cluster-based genetic algorithm with elitist selection (CBGA-ES) with the aim to reduce such randomness for supporting multi-objective test optimization. We empirically compared CBGA-ES with random search, greedy (as baselines) and four commonly used multi-objective search algorithms (e.g., NSGA-II) using two industrial and one real world test optimization problem, i.e., test suite minimization, test case prioritization, and test case selection. The results showed that CBGA-ES significantly outperformed the baseline algorithms (e.g., greedy), and the four selected search algorithms for all the three test optimization problems. CBGA-ES managed to outperform more than 75% of the objectives for all the four algorithms in each test optimization problem. Moreover, CBGA-ES was able to improve the quality of the solutions for an average of 32.5% for each objective as compared to the four algorithms for the three test optimization problems.

关键词： multi-objective test optimization Search multi-objective genetic algorithm Cluster

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