Test Case Suite Reduction of High Dimensional Data by Automatic Subspace Clustering
Bhawna Jyoti1, Aman Kumar Sharma2
1Bhawna Jyoti, Computer Science Department MM University, Solan, India.
2Aman Kumar Sharma, Computer Science Department, Himachal Pradesh University ,Shimla, India.
Manuscript received on March 02, 2014. | Revised Manuscript received on March 05, 2014. | Manuscript published on March 05, 2014. | PP: 159-162 | Volume-4 Issue-1, March 2014. | Retrieval Number: A2116034114 /2014©BEIESP
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© The Authors. Published By: 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: Mostly, testing techniques are designed for data which are having low dimensional space and less intention is paid to the testing of high dimensional data. In this paper, data undergoes a process of dimensionality reduction by principal component analysis (PCA) which leads to the automate subspace clustering of data. The combination of distributed based approach and coverage based approach is used to test the test cases sampled from each cluster formed. The contribution of this paper is related to the dimensionality reduction as well as test case suite reduction by discovering patterns in software testing in a rigorous manner.
Keywords: Dimensionality reduction using PCA, clustering, the test suite minimization.