Abstract
Software regression testing is very expensive and time-consuming process. It is also an essential part of the software development life cycle. The main objective of regression testing is to run all possible combinations of test cases in a test suite. This process requires a large amount of efforts as well as time. Hence, to overcome the human effort and time, there is need for prioritization of test cases. The objective of this research is to execute those test cases have high priority before low priority of test case. We have proposed a new test case prioritization technique using cat swarm optimization (CSO) with clustering approach. CSO is a latest meta-heuristic algorithm based on the behavior of cats. In this paper, we present a clustering-based test case prioritization technique. The method consists of clustering the test cases based on fault detected by test cases. To measure the effectiveness of this algorithm, we used average percentage fault detection (APFD) metric. Moreover, experimental results show that CSO algorithm is an effective and efficient method to prioritize the test cases.
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Yadav, D.K., Dutta, S. (2019). A New Cluster-Based Test Case Prioritization Using Cat Swarm Optimization Technique. In: Nath, V., Mandal, J. (eds) Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems. Lecture Notes in Electrical Engineering, vol 556. Springer, Singapore. https://doi.org/10.1007/978-981-13-7091-5_36
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DOI: https://doi.org/10.1007/978-981-13-7091-5_36
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