A novel test data generation approach based upon mutation testing by using artificial immune system for Simulink models

Abstract

Software testing is costly, labor intensive, and time consuming activity. Test data generation is one of the most important steps in testing process in terms of revealing faults in software. A set of test data is considered as good quality if it is highly capable of discovering possible faults. Mutation analysis is an effective way to assess the quality of a test set. Nowadays, high level models such as Simulink are widely used to reduce the time of software development in many industrial fields. This also allows faults to be detected at the earlier stages. Verification and validation of Simulink models are becoming vital to users. In this paper, we propose the automated test data generation approach based on mutation testing for Simulink models by using Artificial Immune System (AIS) in order to evolve test data. The approach was integrated into the MuSimulink tool [15]. It has been applied to some different case studies and the obtained results are very promising.