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

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

Abstract

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.

Citation

Chen, J., Zhu, L., Chen, T. Y., Towey, D., Kuo, F., Huang, R., & Guo, Y. (2018). Test case prioritization for object-oriented software: an adaptive random sequence approach based on clustering. Journal of Systems and Software, 135, https://doi.org/10.1016/j.jss.2017.09.031

• Journal Article Type: Article
• Acceptance Date: Sep 30, 2017
• Online Publication Date: Oct 7, 2017
• Publication Date: Jan 31, 2018
• Deposit Date: May 15, 2018
• Publicly Available Date: Oct 8, 2018
• Journal: Journal of Systems and Software
• Print ISSN: 0164-1212
• Electronic ISSN: 0164-1212
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 135
• DOI: https://doi.org/10.1016/j.jss.2017.09.031
• Keywords: Object-oriented software; Adaptive random sequence; Test cases prioritization; Cluster analysis; Test cases selection
• Public URL: https://nottingham-repository.worktribe.com/output/907629
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0164121217302170?via%3Dihub