Aimed at verifying safety properties and improving simulation coverage for hybrid systems models of embedded control software, we propose a technique that combines numerical simulation and symbolic methods for computing state-sets. We consider systems with linear dynamics described in the commercial modeling tool Simulink/Stateflow. Given an initial state x, and a discrete-time simulation trajectory, our method computes a set of initial states that are guaranteed to be equivalent to x, where two initial states are considered to be equivalent if the resulting simulation trajectories contain the same discrete components at each step of the simulation. We illustrate the benefits of our method on two case studies. One case study is a benchmark proposed in the literature for hybrid systems verification and another is a Simulink demo model from Mathworks. Disciplines Computer SciencesThis conference paper is available ABSTRACTAimed at verifying safety properties and improving simulation coverage for hybrid systems models of embedded control software, we propose a technique that combines numerical simulation and symbolic methods for computing state-sets. We consider systems with linear dynamics described in the commercial modeling tool Simulink/Stateflow. Given an initial state x, and a discrete-time simulation trajectory, our method computes a set of initial states that are guaranteed to be equivalent to x, where two initial states are considered to be equivalent if the resulting simulation trajectories contain the same discrete components at each step of the simulation. We illustrate the benefits of our method on two case studies. One case study is a benchmark proposed in the literature for hybrid systems verification and another is a Simulink demo model from Mathworks.

SUMMARYModel‐based test generation techniques based on random input generation and guided simulation do not satisfy the demands of high test coverage and completeness guarantees as required by safety‐critical applications. Recently, test generation techniques based on model checking have been reported to bridge this gap. To evaluate the effectiveness of these techniques, an in‐house tool suite, AutoMOTGen, has been developed for Simulink/Stateflow and applied on real‐life case studies at General Motors. This paper outlines the test generation methodology of AutoMOTGen and gives a comparative study with a commercial, primarily random input‐based, test generation tool on the same set of examples. The results indicate that in terms of coverage, model checking‐based techniques complement the random input‐based techniques. In addition, they provide proofs for unreachability that can aid in debugging the models. Therefore, it is recommended that model checking‐based tools be utilized to complement and enhance the effectiveness of model‐based testing methods in safety‐critical systems engineering. Copyright © 2013 John Wiley & Sons, Ltd.

Abstract. Typically, a combination of manual and automated transformations is applied when algorithms for digital signal processing are adapted for energy and performance-efficient embedded systems. This poses severe verification problems. Verification becomes easier after converting the code into dynamic single-assignment form (DSA). This paper describes a method to prove equivalence between two programs in DSA where subscripts to array variables and loop bounds are (piecewise) affine expressions. For such programs, geometric modeling can be used and it can be shown, for groups of elements at once, that the outputs in both programs are the same function of the inputs.

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