Mutation-based Test Data Generation for Simulink Models using Genetic Algorithm and Simulated Annealing

Software testing is costly, labor intensive, and time consuming. Modern testing requires faults to be discovered at the earliest possible stages to decrease the cost of fixing errors in software development process. Thus, high level models such as Simulink models have become the focus of much verification effort and research. Mutation testing is a powerful and effective testing technique in terms of process automation and faults detection. Test case generation for Simulink that achieves a high mutation score is complicated. In this paper, we propose the automated test data generation approach based on mutation testing for Simulink models by using Genetic Algorithm (GA) and Simulated Annealing (SA) in order to improve the quality of test data. The approach has been applied to some different case studies and the obtained results are very promising. Keywords-Mutation testing; Test data generation; Genetic Algorithm; Simulated Annealing; Simulink