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Discrete cuckoo search algorithms for test case prioritization
Applied Soft Computing ( IF 8.7 ) Pub Date : 2021-06-09 , DOI: 10.1016/j.asoc.2021.107584
Anu Bajaj , Om Prakash Sangwan

Regression testing is an essential aspect of the software development lifecycle. As the software evolves, the test suite grows, hence the cost and effort to retest the software. Test case prioritization is one of the mitigation techniques for regression testing. It ranks the test cases to maximize the desired properties, e.g., detecting faults early. The efficiency and effectiveness of test case prioritization techniques can be enhanced using optimization algorithms. Nature-inspired algorithms are gaining more attention due to their easy implementation and quality of the solutions. This paper proposes the discrete cuckoo search algorithm for test case prioritization. The prioritization problem deals with ordering the test cases. Therefore, a new adaptation strategy using asexual genetic reproduction is introduced to convert real numbers into permutation sequences. Furthermore, the cuckoo search algorithm’s effectiveness is extended with the genetic algorithm’s mutation operator to balance the trade-off between exploration and exploitation. An in-depth comparative study on four case studies is conducted between the proposed algorithms, existing state-of-the-art algorithms and baseline approach. Statistical investigation confirms that the proposed hybrid cuckoo search algorithm outperforms the genetic algorithm, particle swarm optimization, ant colony optimization, tree seed algorithm and random search by 4.29%, 5.52%, 8.38%, 2.74% and 10.80%, respectively.



中文翻译:

用于测试用例优先级排序的离散布谷鸟搜索算法

回归测试是软件开发生命周期的一个重要方面。随着软件的发展,测试套件也在增长,因此重新测试软件的成本和工作量也随之增加。测试用例优先级排序是回归测试的缓解技术之一。它对测试用例进行排序以最大化所需的属性,例如,及早检测故障。使用优化算法可以提高测试用例优先排序技术的效率和有效性。受自然启发的算法因其易于实施和解决方案的质量而受到越来越多的关注。本文提出了用于测试用例优先级排序的离散布谷鸟搜索算法。优先级问题处理测试用例的排序。所以,引入了一种使用无性遗传繁殖的新适应策略,将实数转换为排列序列。此外,通过遗传算法的变异算子扩展了布谷鸟搜索算法的有效性,以平衡探索和开发之间的权衡。对所提出的算法、现有的最先进算法和基线方法之间的四个案例研究进行了深入的比较研究。统计调查证实,所提出的混合布谷鸟搜索算法分别优于遗传算法、粒子群优化、蚁群优化、树种子算法和随机搜索4.29%、5.52%、8.38%、2.74%和10.80%。杜鹃搜索算法的有效性通过遗传算法的变异算子进行扩展,以平衡探索和开发之间的权衡。在所提出的算法、现有的最先进算法和基线方法之间对四个案例研究进行了深入的比较研究。统计调查证实,所提出的混合布谷鸟搜索算法分别优于遗传算法、粒子群优化、蚁群优化、树种子算法和随机搜索4.29%、5.52%、8.38%、2.74%和10.80%。杜鹃搜索算法的有效性通过遗传算法的变异算子进行扩展,以平衡探索和开发之间的权衡。在所提出的算法、现有的最先进算法和基线方法之间对四个案例研究进行了深入的比较研究。统计调查证实,所提出的混合布谷鸟搜索算法分别优于遗传算法、粒子群优化、蚁群优化、树种子算法和随机搜索4.29%、5.52%、8.38%、2.74%和10.80%。

更新日期:2021-06-17
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