Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Han, J.W., Micheline, K.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Machine Learning: Proceedings of the Fourteenth International Conference, pp. 412–420 (1997)
Yoo, S., Harman, M.: Regression testing minimization, selection and prioritization: a survey. Software Testing, Verification and Reliability (2010)
Zhang, D.P., Nie, C.G., Xu, B.W.: Optimal Allocation of Test Case Considering Testing-Resource in Partition Testing. The journal of NanJing university, Natural Sciences, 554–555 (2005)
Orso, A., Harrold, M.j.: Using component meta-content to support the regression testing of component based software. In: Proc. of IEEE Int. Conf on Software Maintenance, pp. 716–725 (2001)
Tsai, W.T., Zhou, X.Y., Paul, R.A., Chen, Y.N., Bai, X.Y.: A Coverage Relationship Model for Test Case Selection and Ranking for Multi-version Software. In: 10th IEEE High Assurance System Engineering Symposium, pp. 105–112 (2005)
Cai, K.F.: A Critical Review on Software Reliability Modeling Reliability Engineering and System Safety, vol. 1(32) (1991)
Last, M.: The Data Mining Approach to Automated software Testing, Conference on Knowledge Discovery in Data. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 24–27 (2003)
Mockus, A., Weiss, D.M., Zhang, P.: Understanding and predicting effort in software projects. In: Proceedings of the 25th International Conference on Software Engineering, pp. 274–284 (2003)
Dick, S., Meeks, A., Last, M., Bunke, H., Kandel, A.: Mining in Software Metric Databases. Fuzzy Sets and Systems 145(1), 81–110 (1998)
Ostrand, T., Weyuker, E., Bell, R.M.: Predicting the location and number of faults in large software system. IEEE Transactions in Software Engineering 31(4), 340–355 (2005)
Sergiy, A., Vilkomir, S.W.T., Jesse Poore, H.: Combinatorial test case selection with Markovian usage models. Fifth International Conference on Information Technology: New Generations, 3–8 (2008)
Xiang, I., Yang, C.Z., Lu, T.K., Jaygarl, H.J.: Implicit Social Network Model for Predicting and Tracking the Location of Faults. In: The 32nd Annual IEEE International Computer Software and Applications Conference, pp. 136–143. IEEE Computer Society, Los Alamitos (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yu, L., Xu, L., Tsai, WT. (2010). Time-Constrained Test Selection for Regression Testing. In: Cao, L., Zhong, J., Feng, Y. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17313-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-17313-4_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17312-7
Online ISBN: 978-3-642-17313-4
eBook Packages: Computer ScienceComputer Science (R0)