Title: Mutation-based genetic algorithm for efficiency optimisation of unit testing

Authors: Rijwan Khan; Mohd Amjad

Addresses: Department of Computer Science and Engineering, Jamia Milia Islamia University, New Delhi, India ' Department of Computer Science and Engineering, Faculty of Engineering, Jamia Millia Islamia University, New Delhi, India

Abstract: Fault in a software program can be detected by mutation testing. However, mutation testing is an expensive process in a software testing domain. In this paper, we have introduced a method based on genetic algorithm and mutation analysis for unit testing process. Software industry produces high quality software in which software testing has an important role. First, we make a program/software and intent some mutant in this program/software, find most critical path and optimise test cases using genetic algorithm for the unit testing. Initially generated test cases are refined using genetic algorithm. We use a mutant function for measuring the adequacy of the test case set. The given mutant function is used to calculate a mutant score. We have achieved 100% path coverage and boundary coverage using mutation testing. The objective is to produce a set of good test cases for killing one or more undesired mutants and produces different mutant from original software/program. Unlike simple algorithms, genetic algorithms provide suitability for reducing the data generation at a comparable cost. An optimised test case has been generated by proposed approach for cost reduction and revealing or killing undesired test cases.

Keywords: genetic algorithm; GA; software testing; ST; automatic test case coverage; ATCC; boundary value analysis; BVA; mutation testing; MT.

DOI: 10.1504/IJAIP.2019.098563

International Journal of Advanced Intelligence Paradigms, 2019 Vol.12 No.3/4, pp.254 - 265

Received: 27 Jan 2016
Accepted: 16 Apr 2016
Published online: 28 Mar 2019 *

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