Component based and model based development (CBD vs. MBD), in their various interpretations, are in focus in many efforts in order to better handle the efficient development of increasingly complex embedded systems. We elaborate on what CBD and MBD represent, on their differences and similarities. Although CBD represents a bottom-up approach whereas MBD is more top-down in nature, it turns out that the concepts have much in common and can benefit from cross-fertilization. In particular, MBD requires improved handling of ýmodelý components, and CBD requires improved component models to assure component composition and reuse. We discuss their mutual opportunities and other relationships.

Cyber-physical systems (CPSs) integrate heterogeneous systems and process sensor data using digital services. As the complexity of CPS increases, it becomes more challenging to efficiently formalize the integrated multidomain views with flexible automated verification across the entire lifecycle. This article illustrates a model-based systems engineering tool-chain to support CPS development with an emphasis on automated parameter value selection for co-simulation. First, a domain-specific modeling approach is introduced to support the formalizations of CPS artifacts, development processes, and simulation configurations. The domain-specific models are used as the basis to generate a Web-based process management system for automated parameter value selections, which coordinates Open Services for Lifecycle Collaboration services of development information and technical resources (models, data, and tools) in order to support automated co-simulation. The services are deployed by a service orchestrator based on a decision-making algorithm for parameter value selection. Finally, developers make use of the WPMS to implement simulations and to select system parameter values for co-simulation automatically. The approach is illustrated by a case study on auto-braking system development and we evaluate the efficiency of this tool-chain by both qualitative and quantitative methods. The results show that parameter values are selected more efficiently and effectively when implementing co-simulations using our tool-chain.