TY - GEN
T1 - Time-constrained test selection for regression testing
AU - Yu, Lian
AU - Xu, Lei
AU - Tsai, Wei Tek
N1 - Funding Information:
Acknowledgements. This work is partially supported by the National Science Foundation of China (No.60973001), IBM China Research Lab (No.20090101), and U.S. Department of Education FIPSE project. The authors would thank Jingtao zhao, Hui Lv, Ting Xu for working on the empirical study described in this paper.
Funding Information:
Our future work will collect more datasets on integration testing, increasing the accuracy of predicting results. We will gather new test-case data, classify them with Acknowledgements. This work is partially supported by the National Science Foundation of China (No.60973001), IBM China Research Lab (No.20090101), and U.S. Department of Education FIPSE project. The authors would thank Jingtao zhao, Hui Lv, Ting Xu for working on the empirical study described in this paper.
PY - 2010
Y1 - 2010
N2 - The strategy of regression test selection is critical to a new version of software product. Although several strategies have been proposed, the issue, how to select test cases that not only can detect faults with high probability but also can be executed within a limited period of test time, remains open. This paper proposes to utilize data-mining approach to select test cases, and dynamic programming approach to find the optimal test case set from the selected test cases such that they can detect most faults and meet testing deadline. The models have been applied to a large financial management system with a history of 11 releases over 5 years.
AB - The strategy of regression test selection is critical to a new version of software product. Although several strategies have been proposed, the issue, how to select test cases that not only can detect faults with high probability but also can be executed within a limited period of test time, remains open. This paper proposes to utilize data-mining approach to select test cases, and dynamic programming approach to find the optimal test case set from the selected test cases such that they can detect most faults and meet testing deadline. The models have been applied to a large financial management system with a history of 11 releases over 5 years.
KW - P-measure
KW - Test case selection
KW - Time-constrained
KW - regression testing
KW - test case classification
UR - http://www.scopus.com/inward/record.url?scp=78650186589&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78650186589&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-17313-4_23
DO - 10.1007/978-3-642-17313-4_23
M3 - Conference contribution
AN - SCOPUS:78650186589
SN - 3642173128
SN - 9783642173127
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 221
EP - 232
BT - Advanced Data Mining and Applications - 6th International Conference, ADMA 2010, Proceedings
T2 - 6th International Conference on Advanced Data Mining and Applications, ADMA 2010
Y2 - 19 November 2010 through 21 November 2010
ER -