Testing implementation approach case study

In this paper, we report on a case study developed in the SafeCer project, where we combined contract-based design and model-based testing. A contract-based approach has been used to formalize the safety requirements to detect communication failures. The formal specification shows under which assumptions the AUTOSAR protection mechanism fulfills these requirements. A model-based testing approach has been used to test the software implementing such protection mechanism. The model used for testing has been model checked against the contract specification ensuring that the system-level safety requirements are met. 

In this paper we show how PCG techniques can be adapted to test AR control software, and demonstrate this by application to a small case study. In section 2 we identify the requirements for such an approach, in section 3 we explain what we have implemented, and in section 4 we describe an initial experimental evaluation. Section 5 compares our approach against previous work section 6 summarises the paper and identifies future possibilities. 

The application of ANN for a representation of reaction kinetics can be a very promising method to solve modelling problems. Besides intrinsic kinetics also internal diffusion resistances can be included into the neural network based model. This approach significantly reduces the time required for experimental studies. Despite that neural networks do not help to understand and develop a real reaction mechanism, they make the prediction of the reactor behaviour possible. This approach can be essential in the case of complex or uncertain kinetics - e.g. for polymerization reactions. In this study the neural network approach has been tested for a batch reactor. A trained network can be successfully implemented into any type reactor model. 

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