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An Effective Regression Test Case Selection Using Hybrid Whale Optimization Algorithm

An Effective Regression Test Case Selection Using Hybrid Whale Optimization Algorithm
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Author(s): Arun Prakash Agrawal (Guru Gobind Singh Indraprastha University, New Delhi, India), Ankur Choudhary (Amity University Uttar Pradesh, Noida, India) and Arvinder Kaur (Guru Gobind Singh Indraprastha University, New Delhi, India)
Copyright: 2020
Volume: 11
Issue: 1
Pages: 15
Source title: International Journal of Distributed Systems and Technologies (IJDST)
Editor(s)-in-Chief: Nik Bessis (Edge Hill University, UK)
DOI: 10.4018/IJDST.2020010105

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Abstract

Test suite optimization is an ever-demanded approach for regression test cost reduction. Regression testing is conducted to identify any adverse effects of maintenance activity on previously working versions of the software. It consumes almost seventy percent of the overall software development lifecycle budget. Regression test cost reduction is therefore of vital importance. Test suite optimization is the most explored approach to reduce the test suite size to re-execute. This article focuses on test suite optimization as a regression test case selection, which is a proven N-P hard combinatorial optimization problem. The authors have proposed an almost safe regression test case selection approach using a Hybrid Whale Optimization Algorithm and empirically evaluated the same on subject programs retrieved from the Software Artifact Infrastructure Repository with Bat Search and ACO-based regression test case selection approaches. The analyses of the obtained results indicate an improvement in the fault detection ability of the proposed approach over the compared ones with significant reduction in test suite size.

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