The Two-hemisphere model-driven (2HMD) approach assumes modelling and use of procedural and conceptual knowledge on an equal and related basis.This differentiates 2HMD approach from pure procedural, pure conceptual, and object oriented approaches.The approach may be applied in the context of modelling of a particular business domain as well as in the context of modelling the knowledge about the domain.Cyber-physical systems are heterogeneous systems, which require multi-disciplinary approach to their modelling.Modelling of cyber-physical systems by 2HMD approach gives an opportunity to transparently compose and analyse system components to be provided and components actually provided, and, thus, to identify and fill the gaps between desirable and actual system content.

Smart Cyber-Physical Systems (sCPS) are complex distributed decentralized systems of cooperating components. They typically operate in uncertain environments and thus require means for managing variability at run-time. Architectural modes have traditionally been a proven means for the runtime variability. They are easy to understand, easy to realize in resource-constrained systems and (contrary to more sophisticated methods of learning) provide an explicit specification that can be inspected and validated at design time. However, in uncertain environments (which is the case of sCPS), they tend to lack expressivity to take into account the level of uncertainty and factor it in the mode-switching logic. In this paper we present a rich language to specify mode-switch guards. The semantics of the language is based on statistical tests, which, as we show, is a convenient way to reason about uncertainty in the state of the environment.

Cyber-physical systems (CPS) are systems of cooperating autonomous components which closely interact with and control the physical environment. Being distributed and typically based on periodic activities, CPS have to cope with the problem that data capturing a distributed state of the system and its environment are inherently inaccurate (they represent belief on the state). In particular, this poses a problem when dependability is being pursued. In this paper we address this issue by modeling belief at the architecture level. In particular, we enhance the architecture by models describing belief inaccuracy over time. We exploit these models to quantify at runtime the impact of belief staleness on its inaccuracy. We then use this quantification to drive architectural adaptation with the aim to increase dependability of the running CPS system.