Improving mutant generation for Simulink models using genetic algorithm

Simulink is one of the most popular tools used to design dynamic models for industrial complex systems. Mutation testing is a fault-based technique widely used for testing software. However, the testing cost is often very high. A tool which allows automatic testing for Simulink models with only a subset of mutants to reduce testing costs and time while ensuring the quality of software products is very neccessary. This paper proposes the use of genetic algorithm to minimize the number of generated mutants without losing the efficiency of mutation testing approach. The experiment confirms that the proposed method contributed to the reduction in time and costs for mutation testing process.