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Test case prioritization for object-oriented software: An adaptive random sequence approach based on clustering

机译：面向对象软件的测试用例优先级划分：基于聚类的自适应随机序列方法

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摘要

Test case prioritization (TCP) attempts to improve fault detection effectiveness by scheduling the important test cases to be executed earlier, where the importance is determined by some criteria or strategies. Adaptive random sequences (ARSs) can be used to improve the effectiveness of TCP based on white-box information (such as code coverage information) or black-box information (such as test input information). To improve the testing effectiveness for object-oriented software in regression testing, in this paper, we present an ARS approach based on clustering techniques using black-box information. We use two clustering methods: (1) clustering test cases according to the number of objects and methods, using the K-means and K-medoids clustering algorithms; and (2) clustered based on an object and method invocation sequence similarity metric using the K-medoids clustering algorithm. Our approach can construct ARSs that attempt to make their neighboring test cases as diverse as possible. Experimental studies were also conducted to verify the proposed approach, with the results showing both enhanced probability of earlier fault detection, and higher effectiveness than random prioritization and method coverage TCP technique.
机译：测试用例优先级排序（TCP）试图通过安排要在较早执行的重要测试用例来提高故障检测的效率，在此情况下，重要性由某些标准或策略确定。自适应随机序列（ARS）可用于基于白盒信息（例如代码覆盖率信息）或黑盒信息（例如测试输入信息）来提高TCP的有效性。为了提高面向对象软件在回归测试中的测试效率，在本文中，我们提出了一种基于黑盒信息的基于聚类技术的ARS方法。我们使用两种聚类方法：（1）根据对象和方法的数量对测试用例进行聚类，使用K-means和K-medoids聚类算法； （2）使用K-medoids聚类算法基于对象和方法调用序列相似性度量进行聚类。我们的方法可以构造ARS，尝试使它们的相邻测试用例尽可能多样化。还进行了实验研究以验证所提出的方法，结果表明与随机优先级划分和方法覆盖TCP技术相比，早期故障检测的概率增加，并且有效性更高。

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  • 来源
    《The Journal of Systems and Software》 |2018年第1期|107-125|共19页
  • 作者

    Jinfu Chen; Lili Zhu; Tsong Yueh Chen; Dave Towey; Fei-Ching Kuo; Rubing Huang; Yuchi Guo;

  • 作者单位

    School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 202000, China;

    School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 202000, China;

    Department of Computer Science and Software Engineering, Swinhurne University of Technology, Hawthorn, 3122, Australia;

    School of Computer Science, University of Nottingham Ningbo China, Ningbo, 3I5700, China;

    Department of Computer Science and Software Engineering, Swinhurne University of Technology, Hawthorn, 3122, Australia;

    School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 202000, China;

    School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 202000, China;

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  • 正文语种 eng
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  • 关键词

    Object-oriented software; Adaptive random sequence; Test cases prioritization; Cluster analysis; Test cases selection;

    机译：面向对象的软件;自适应随机序列;测试案例优先级;聚类分析;测试案例选择;

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