A similarity-based multi-objective test optimization technique using search algorithm

Amir Sohail Habib , Saif Ur Rehman Khan , Shahid Hussain , Naseem Ibrahim , Habib un Nisa , Abdullah Yousafzai
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引用次数: 0

Abstract

Context:

Software undergoes a constant evolution driven by ongoing changes in customer requirements, which enhances the competitive advantage. Regression testing plays a pivotal role by ensuring that modifications have not introduced detrimental effects on the system under test.

Problem:

However, regression testing becomes prohibitively expensive as the software grows in complexity and the size of the test suite also expands. Moreover, keeping the test cases up-to-date and managing the relevant test data can become a laborious and challenging task. Hence, it is required to optimize the test suite by finding a diverse subset of test cases having high code coverage, fault-detection rate, and minimal execution time.

Objective:

To solve the regression test optimization problem, the researchers have proposed various approaches including greedy algorithms, search-based algorithms, and clustering algorithms. However, existing approaches lack in finding the global optimal solution and are mostly focused on the single-objective test optimization problem. Inspired by this, we propose a Similarity-based Multi-Objective Optimization Technique (SMOOT) for test suite reduction using a Grey Wolf Optimizer (GWO) algorithm. The proposed technique employs different similarity metrics, including Cosine Similarity, Euclidean Distance, Jaccard Similarity, Manhattan Distance, and Minkowski Distance, to evaluate the similarity score of the tests. This ensures a comprehensive assessment of test diversity to achieve high code coverage and fault-detection rate while minimizing the test execution cost.

Method:

We evaluated the performance of GWO with state-of-the-art search-based algorithms using three varying types of case studies. Similarly, to evaluate the similarity score of the considered search algorithms, we employed state-of-the-art similarity measures.

Results:

The experimental results revealed that GWO significantly outperformed the considered search algorithms by attaining high code coverage and fault-detection rate while minimizing the test execution time. Moreover, we found that GWO attained a higher similarity score than the other considered search algorithms using the employed similarity measures.

Conclusion:

Based on the attained results, we believe that the proposed technique could be useful for the researchers and practitioners by effectively handling multi-objective regression test optimization problem.
使用搜索算法的基于相似性的多目标测试优化技术
背景:在客户需求不断变化的推动下,软件经历着持续演进,从而增强了竞争优势。问题:然而,随着软件复杂性的增加和测试套件规模的扩大,回归测试的成本变得非常高昂。此外,及时更新测试用例和管理相关测试数据也会成为一项费力且具有挑战性的任务。目标:为了解决回归测试优化问题,研究人员提出了各种方法,包括贪婪算法、基于搜索的算法和聚类算法。然而,现有的方法缺乏全局最优解,而且大多集中在单目标测试优化问题上。受此启发,我们提出了一种基于相似性的多目标优化技术(SMOOT),利用灰狼优化器(GWO)算法减少测试套件。该技术采用了不同的相似度量,包括余弦相似度、欧几里得距离、杰卡德相似度、曼哈顿距离和闵科夫斯基距离,来评估测试的相似度得分。方法:我们使用三种不同类型的案例研究,评估了 GWO 与基于搜索的先进算法的性能。同样,为了评估所考虑的搜索算法的相似性得分,我们采用了最先进的相似性度量方法。结果:实验结果表明,GWO 的性能明显优于所考虑的搜索算法,它既能获得较高的代码覆盖率和故障检测率,又能最大限度地减少测试执行时间。结论:基于所获得的结果,我们认为所提出的技术可以有效地处理多目标回归测试优化问题,对研究人员和从业人员非常有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
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