Mutation-based genetic algorithm for efficiency optimisation of unit testing

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.