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Paper Details
Paper Title
A Novel Density based K-means Clustering for Test Case Prioritization in Regression Testing
Authors
Isha Luthra , Harsimranjeet Kaur
Abstract
In this paper, we work on improving the test case prioritization on the basis of clustering approach. A novel density based k-means clustering approach is used to make clusters of different test cases on the basis of statement coverage. Then, prim’s algorithm is used to find out the minimum path between different test cases according to their coverage information. Test cases are select from every cluster; which have maximum coverage information. According to Prim’s algorithm, we will find the tree of test cases; this technique reduces the test cases numbers. Only those test cases are selected which have maximum coverage information. It will reduce the effort, cost and time also.
Keywords- Test Case Prioritization, Density based K means, Regression Testing
Publication Details
Unique Identification Number - IJEDR1504027Page Number(s) - 184-189Pubished in - Volume 3 | Issue 4 | 31 October 2015DOI (Digital Object Identifier) - Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
Isha Luthra , Harsimranjeet Kaur, "A Novel Density based K-means Clustering for Test Case Prioritization in Regression Testing ", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.3, Issue 4, pp.184-189, 31 October 2015, Available at :http://www.ijedr.org/papers/IJEDR1504027.pdf
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