Test case prioritization for object-oriented software: an adaptive random sequence approach based on clustering
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
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Elsevier
2018
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| Online Access: | https://eprints.nottingham.ac.uk/51786/ |
| _version_ | 1848798574557528064 |
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| author | Chen, Jinfu Zhu, Lili Chen, Tsong Yueh Towey, Dave Kuo, Fei-Ching Huang, Rubing Guo, Yuchi |
| author_facet | Chen, Jinfu Zhu, Lili Chen, Tsong Yueh Towey, Dave Kuo, Fei-Ching Huang, Rubing Guo, Yuchi |
| author_sort | Chen, Jinfu |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:21:56Z |
| format | Article |
| id | nottingham-51786 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:21:56Z |
| publishDate | 2018 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-517862020-05-04T19:28:54Z https://eprints.nottingham.ac.uk/51786/ 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 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. Elsevier 2018-01-31 Article PeerReviewed Chen, Jinfu, Zhu, Lili, Chen, Tsong Yueh, Towey, Dave, Kuo, Fei-Ching, 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 Object-oriented software; Adaptive random sequence; Test cases prioritization; Cluster analysis; Test cases selection https://www.sciencedirect.com/science/article/pii/S0164121217302170?via%3Dihub doi:10.1016/j.jss.2017.09.031 doi:10.1016/j.jss.2017.09.031 |
| spellingShingle | Object-oriented software; Adaptive random sequence; Test cases prioritization; Cluster analysis; Test cases selection Chen, Jinfu Zhu, Lili Chen, Tsong Yueh Towey, Dave Kuo, Fei-Ching Huang, Rubing Guo, Yuchi Test case prioritization for object-oriented software: an adaptive random sequence approach based on clustering |
| title | Test case prioritization for object-oriented software: an adaptive random sequence approach based on clustering |
| title_full | Test case prioritization for object-oriented software: an adaptive random sequence approach based on clustering |
| title_fullStr | Test case prioritization for object-oriented software: an adaptive random sequence approach based on clustering |
| title_full_unstemmed | Test case prioritization for object-oriented software: an adaptive random sequence approach based on clustering |
| title_short | Test case prioritization for object-oriented software: an adaptive random sequence approach based on clustering |
| title_sort | test case prioritization for object-oriented software: an adaptive random sequence approach based on clustering |
| topic | Object-oriented software; Adaptive random sequence; Test cases prioritization; Cluster analysis; Test cases selection |
| url | https://eprints.nottingham.ac.uk/51786/ https://eprints.nottingham.ac.uk/51786/ https://eprints.nottingham.ac.uk/51786/ |