Simulink is a popular tool for modeling cyber-physical systems. As more models are produced in industry, automated quality assurance of models becomes increasingly important. This paper describes an empirical evaluation of four methods for the coverage testing of Simulink models: A) SimuLink Design Verifier (SLDV), a dedicated official tool; B) Template-Based Monte-Carlo (TBMC) method, a random test generation method that utilizes input signal templates; C) SMT- Based Model Checking (SBMC) method that conducts static analysis via encoding models into logic formulas; and D) a hybrid method of B and C. Based on the evaluation results, we carefully designed the hybrid method to complement the features of TBMC and SBMC. In the experiments, we have applied the methods to fourteen models and evaluated their performance. The results show that the hybrid method achieved better results than SLDV for several models.

In this paper, we develop a combinatorial testing technique for tree-structured test models. First, we generalize our previous test models for combinatorial testing based on and-xor trees with constraints limited to a syntactic subset of propositional logic, to allow for constraints in full propositional logic. We prove that the generalized test models are strictly more expressive than the limited ones. Then we develop an algorithm for combinatorial testing for the generalized models, and show its correctness and computational complexity. We apply a tool based on our algorithm to an actual ticket gate system that is used by several large transportation companies in Japan. Experimental results show that our technique outperforms existing techniques.

AUTOSAR multicore RTOS is a safety-critical concurrent system, for which high quality is required. A conformance test is important to ensure the quality of the software, but the conventional test is low in coverage and high in cost. In this paper, we present a formal model-based test for multicore RTOS that supports AUTOSAR specifications. First, we developed a formal model. With the model, we developed a test case generator, from which an entire test suite can be extracted. Moreover, we proposed a test program generator, with which optimal executable test programs can be generated fully automatically. Both of the generators are assisted with model checking on the formal model. Bug analysis also becomes easy. Our method demonstrated its advantage over conventional testing by finding 33 test cases for three system service calls, whereas a conventional test carried out by a development team found only 10 test cases. Our method can improve the coverage of the test, clearly saving in cost and development time. It is expected to significantly improve the testing of the AUTOSAR multicore RTOS.