期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2014
卷号：4
期号：1
页码：159-162
出版社：International Journal of Soft Computing & Engineering
摘要：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.
关键词：Dimensionality reduction using PCA; clustering; the test suite minimization.
