TY - GEN
T1 - An Adaptive Sequence Approach for OOS Test Case Prioritization
AU - Chen, Jinfu
AU - Zhu, Lili
AU - Chen, Tsong Yueh
AU - Huang, Rubing
AU - Towey, Dave
AU - Kuo, Fei Ching
AU - Guo, Yuchi
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/16
Y1 - 2016/12/16
N2 - Test case prioritization (TCP) attempts to improve fault detection effectiveness by scheduling important test cases earlier, where important is determined by some criteria and strategy. Adaptive random sequences (ARSs) may be applied to improve the effectiveness of TCP in black-box testing. In this paper, to improve the effectiveness of TCP for object-oriented software, we present an ARS approach based on clustering techniques. In the proposed approach, test cases are clustered according to the number of objects and methods, using two clustering algorithms - K-means and K-medoids. Our proposed sampling strategy can construct ARSs within the clustering framework, constructing two ARS sequences based on the two clustering algorithms, which results in generated test cases with different execution sequences. We also report on experimental studies to verify the proposed approach, with the results showing that our approach can enhance the probability of earlier fault detection, and deliver higher effectiveness than random prioritization.
AB - Test case prioritization (TCP) attempts to improve fault detection effectiveness by scheduling important test cases earlier, where important is determined by some criteria and strategy. Adaptive random sequences (ARSs) may be applied to improve the effectiveness of TCP in black-box testing. In this paper, to improve the effectiveness of TCP for object-oriented software, we present an ARS approach based on clustering techniques. In the proposed approach, test cases are clustered according to the number of objects and methods, using two clustering algorithms - K-means and K-medoids. Our proposed sampling strategy can construct ARSs within the clustering framework, constructing two ARS sequences based on the two clustering algorithms, which results in generated test cases with different execution sequences. We also report on experimental studies to verify the proposed approach, with the results showing that our approach can enhance the probability of earlier fault detection, and deliver higher effectiveness than random prioritization.
KW - Adaptive random sequence
KW - Cluster analysis
KW - Object-oriented software
KW - Test cases prioritization
KW - Test cases selection
UR - http://www.scopus.com/inward/record.url?scp=85009726761&partnerID=8YFLogxK
U2 - 10.1109/ISSREW.2016.29
DO - 10.1109/ISSREW.2016.29
M3 - Conference contribution
AN - SCOPUS:85009726761
T3 - Proceedings - 2016 IEEE 27th International Symposium on Software Reliability Engineering Workshops, ISSREW 2016
SP - 205
EP - 212
BT - Proceedings - 2016 IEEE 27th International Symposium on Software Reliability Engineering Workshops, ISSREW 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 27th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2016
Y2 - 23 October 2016 through 27 October 2016
ER -