New methods and techniques are needed to reduce the very costly integration and test effort (in terms of lead time, costs, resources) in the development of high-tech multi-disciplinary systems. To facilitate this effort reduction, we propose a method called model-based integration. This method allows to integrate formal executable models of system components that are not yet physically realized with available realizations of other components. The combination of models and realizations is then used for early analysis of the integrated system by means of validation, verification, and testing. This analysis enables early detection and prevention of problems that would otherwise occur during real integration, resulting in a significant reduction of effort invested in the the real integration and test phases. This paper illustrates how models of components, developed for model-based integration, can be used for automated model-based testing, which allows time-efficient determination of the conformance of component realizations with respect to their requirements. The combination of model-based integration and model-based testing is practically illustrated in a realistic industrial case study. Results obtained from this study encourage further research on model-based integration as a prominent method to reduce the integration and test effort.
The effort required for integration and testing of high‐tech multi‐disciplinary systems is increasing with each new or upgraded system that is developed. To counter this trend of increasing integration and test lead time and costs, we propose a model‐based integration and testing (MBI&T) method, where formal and executable models of the system components are used to replace the component realizations for early integration and system testing. In this paper, we describe how the integration and testing process currently used in industry can be made more intelligent by applying model‐based techniques from the MBI&T method. We also show how to analyze the necessary trade‐off between the investments needed for model development and the potential effort reduction, using a systematic and automatic integration sequencing method.
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