Title: Searching and evolving test cases using moth flame optimisation for mutation testing

Authors: Shweta Rani; Bharti Suri

Addresses: University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, Delhi – 110078, India; Department of CSE, KIET Group of Institutions, Ghaziabad – 201206, India ' University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, Delhi – 110078, India

Abstract: Generally, mutation testing has been considered effective to test the adequacy of the test suite over a set of artificial faults. These faults are created by applying different mutation operators of mutation testing and manually finding the test suites for revealing these faults is a costly and extensive process. Meta-heuristic techniques may curtail this cost by searching the optimal test suite in search space. These techniques iteratively find and evolve the solution towards an optimal solution. These techniques perform better when blended with mutation testing. This paper proposes and employs a novel mutation-based test generation approach, MFO-MT, inspired by moths' behaviour. Moths fly and search for a better solution in a spiral motion around the flames. The proposed approach is implemented and tested for various Java programs. The approach gives encouraging results when compared with genetic algorithm and random testing.

Keywords: genetic algorithm; meta-heuristic approaches; moth flame optimisation; mutation testing; MuJava; random testing; software testing; swarm intelligence algorithm; test suite generation; test suite optimisation.

DOI: 10.1504/IJIEI.2021.118272

International Journal of Intelligent Engineering Informatics, 2021 Vol.9 No.3, pp.276 - 293

Received: 11 Jan 2021
Accepted: 23 Apr 2021
Published online: 18 Oct 2021 *

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