An Efficient Test Case Prioritization using Hierarchical Clustering for Enhancing Regression Testing
Gayam Rupa Sri1, Addepalli Leela Supriya2, Enukonda Raja3, Avinash Reddy4, Venkata Naresh Mandhala5
1Gayam Rupa Sri, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram (A.P.), India.
2Addepalli Leela Supriya, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram (A.P.), India.
3Enukonda Raja, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram (A.P.), India.
4Avinash Reddy, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram (A.P.), India.
5Venkata Naresh Mandhala, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram (A.P.), India.
Manuscript received on 07 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 914-917 | Volume-8 Issue-5, March 2019 | Retrieval Number: D2706028419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: To enhance the efficiency of regression testing, we propose an experiment order approach dependent on different strategies to arrange test cases into powerful and non-viable gathering. The Hierarchal clustering methodology depends on the inclusion data got for the before arrivals of the program under test. We utilized two regular grouping calculations specifically centroid-based and hierarchal clustering. The experimental investigation results demonstrated the experiment grouping can viably recognize viable experiments with high review proportion and significant exactness rate. The paper additionally explores and looks at the execution of the proposed grouping based methodology with some different components including inclusion criteria, development of highlights, and amount of flaws in the prior discharges.
Keyword: Cluster, Hierarchal Clustering, Regression Testing, Test Cases.
Scope of the Article: Clustering