An Effective Regression Test Case Selection Using Hybrid Whale Optimization Algorithm

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 life...

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Vydáno v:International journal of distributed systems and technologies Ročník 11; číslo 1; s. 53 - 67
Hlavní autoři: Agrawal, Arun Prakash, Choudhary, Ankur, Kaur, Arvinder
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hershey IGI Global 01.01.2020
Témata:
Algorithms
Analysis
Ant colony optimization
Cetacea
Combinatorial analysis
Cost reduction
Fault detection
Mathematical optimization
Optimization algorithms
Regression
Software development
Software testing
India
ISSN:1947-3532, 1947-3540
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Shrnutí: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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ISSN:1947-3532
1947-3540
DOI:10.4018/IJDST.2020010105