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

Chen, Jinfu and Zhu, Lili and Chen, Tsong Yueh and Towey, Dave and Kuo, Fei-Ching and Huang, Rubing and Guo, Yuchi (2018) Test case prioritization for object-oriented software: an adaptive random sequence approach based on clustering. Journal of Systems and Software, 135 . pp. 107-125. ISSN 0164-1212

Full text not available from this repository.

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.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/907629
Keywords: Object-oriented software; Adaptive random sequence; Test cases prioritization; Cluster analysis; Test cases selection
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
Identification Number: https://doi.org/10.1016/j.jss.2017.09.031
Depositing User: QIU, Lulu
Date Deposited: 15 May 2018 08:18
Last Modified: 04 May 2020 19:28
URI: http://eprints.nottingham.ac.uk/id/eprint/51786

Actions (Archive Staff Only)

Edit View Edit View