Paper Info
Title
An Efficient Regression Test Suite Optimization Approach Using Hybrid Spider Monkey Optimization Algorithm
Abstract
Regression testing validates the modified software and safeguards against the introduction of new errors during modification. A number of test suite optimization techniques relying on meta-heuristic techniques have been proposed to find the minimal set of test cases to execute for regression purposes. This paper proposes a hybrid spider monkey optimization based regression test suite optimization approach and empirically compares its performance with three other approaches based on bat search, ant colony, and cuckoo search. The authors conducted an empirical study with various subjects retrieved from software artifact infrastructure repository. Fault coverage and execution time of algorithm are used as fitness measures to meet the optimization criteria. Extensive experiments are conducted to evaluate the performance of the proposed approach with other search-based approaches under study using various statistical tests like m-way ANOVA and post hoc tests including odds ratio. Results indicate the superiority of the proposed approach in most of the cases and comparable in others.
Year
DOI
Venue
2021
10.4018/IJSIR.2021100104
INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH
Keywords
DocType
Volume
Ant Colony Optimization, Bat Search, Cuckoo Search, Regression Testing, Software Maintenance, Spider Monkey Optimization, Test Suite Optimization
Journal
12
Issue
ISSN
Citations 
4
1947-9263
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Arun Prakash Agrawal100.34
Ankur Choudhary200.34
Parma Nand300.34