A search-based framework for automatic testing of MATLAB/Simulink models

Search-based test-data generation has proved successful for code-level testing but almost no search-based work has been carried out at higher levels of abstraction. In this paper the application of such approaches at the higher levels of abstraction offered by MATLAB/Simulink models is investigated and a wide-ranging framework for test-data generation and management is presented. Model-level analogues of code-level structural coverage criteria are presented and search-based approaches to achieving them are described. The paper also describes the first search-based approach to the generation of mutant-killing test data, addressing a fundamental limitation of mutation testing. Some problems remain whatever the level of abstraction considered. In particular, complexity introduced by the presence of persistent state when generating test sequences is as much a challenge at the Simulink model level as it has been found to be at the code level. The framework addresses this problem. Finally, a flexible approach to test sub-set extraction is presented, allowing testing resources to be deployed effectively and efficiently.