Prakash M. Peranandam

An integrated test generation tool for enhanced coverage of simulink/stateflow models

Simulink/Stateflow (SL/SF) is the primary modeling notation for the development of control systems in automotive and aerospace industries. In model based testing, test cases derived from a design model are used to show model-code conformance. Safety standards such as ISO 26262 recommend model based testing to show the conformance of a software with the corresponding model. From our experiments with various test generation techniques, we have observed that their coverage capabilities are complementary in nature. With this observation in mind, we have developed a new tool called SmartTestGen which integrates different test generation techniques. In this paper, we discuss SmartTestGen and the different test generation techniques utilized - random testing, constraint solving, model checking and heuristics. We experimented with 20 production-quality SL/SF models and compared the performance of our tool with that of two prominent commercial tools.

Efficient coverage of parallel and hierarchical stateflow models for test case generation

This paper is concerned with test case generation from Simulink/Stateflow (SL/SF) models with a focus on coverage of SF model elements. Coverage of the SF component in a model is a difficult task because of two primary reasons: (i) the SF component itself may lie deep in the SL/SF model in which case, inputs have to pass through a complex chain of SL blocks to reach the SF block and (ii) nonlinear constraints in the model are difficult to solve using constraint solvers. Hierarchy and parallelism in the SF model add further complexity to the problem. The existing approaches flatten such SF elements, and generate test cases from the flattened finite state machines. Handling of issues (i) and (ii) has already been discussed in earlier research. In this paper, we present a method of covering SF components, which does not require to flatten any hierarchy or parallelism in the components. This not only makes the test case generation problem efficient but also addresses the problem of scalability. We have implemented this method and performed a number of medium‐sized case studies. The results show improved performance over the results obtained by some commercial tools. Copyright

SmartTestGen+: a test suite booster for enhanced structural coverage

Our work concerns with test case generation for structural coverage of Simulink/Stateflow (SL/SF) models. We have developed a tool called SmartTestGen which integrates multiple test generation techniques; experiments show that this tool performs better than some commercial tools. In this paper, we discuss a novel experiment. SmartTestGen uses random testing as one of the testing techniques. The random testing component first generates random test cases; the tool then extends these test cases to cover the uncovered targets. In our experiment, instead of using the random test cases as the initial seed, we use the test cases of an existing test suite. We have evaluated the impact of this modified testing process by considering 20 industrial strength SL/SF models.