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Multi-Objective Optimization Model and Hierarchical Attention Networks for Mutation Testing

Published in IGI Global
Volume: 14
Issue: 1
Pages: 1 - 23

Mutation testing is devised for measuring test suite adequacy by identifying the artificially induced
faults in software. This paper presents a novel approach by considering multiobjectives-based
optimization. Here, the optimal test suite generation is performed using the proposed water cycle
water wave optimization (WCWWO). The best test suites are generated by satisfying the multiobjective
factors, such as time of execution, test suite size, mutant score, and mutant reduction rate.
The WCWWO is devised by a combination of the water cycle algorithm (WCA) and water wave
optimization (WWO). The hierarchical attention network (HAN) is used for classifying the equivalent
mutants by utilizing the MutPy tool. Furthermore, the performance of the developed WCWWO+HAN
is evaluated in terms of three metrics—mutant score (MS), mutant reduction rate (MRR), and fitness—
with the maximal MS of 0.585, higher MRR of 0.397, and maximum fitness of 0.652.

About the journal
JournalInternational Journal of Swarm Intelligence Research
PublisherIGI Global
Open AccessYes