Test Suite Reduction Based on Knowledge Reuse: An Adaptive Elitism Based Intellect Approach (AEBI) using Clustering Technique
Asha N1, Prasanna Mani2
1Asha N, Department of Information Technology and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
2Prasanna Mani, Department of Information Technology and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 1566-1572 | Volume-8 Issue-6, April 2019 | Retrieval Number: F4071048619/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Testing is a Process, Not just a Phase. Software testing is the most challenging part of Software Development. Setting up the test environment is an important part of STLC. In the evolutionary environment the test suite size grows constantly as the new updated version of software evolves. Test suite reduction techniques plays intelligent role in deriving the subset of the original test suite that covers the user requirements without affecting the accuracy of the test result. Owing to time and test resource constraints a candidate set can be formed from the original test suite in order to reduce the data processed by the ACO. In this paper, we attempt to introduce a novel approach an Adaptive Elitism Based Intellect approach (AEBI) using clustering technique to reduce the test suite size. This AEBI approach concentrates on single/multiple test objective. The experimental results are presented by applying the AEBI approach on real time application system. We found that the AEBI approach supports effective usage of test resource and performs much better in close proximity with optimal solutions.
Keyword: Test Suite Reduction, Test Case Optimization, Knowledge Reuse, Elitism, Clustering.
Scope of the Article: Clustering