Keynote Lectures

Abstract
The networking of multi-physics (mechanical, electrical, hydraulic, biochemical, ...) with computational systems (control systems, signal processing, logical inferencing, planning, ...) processes, interacting with often uncertain environments, with human actors, in a socio-economic context, leads to so-called Cyber-Physical Systems (CPS).
The CPS that are engineered today are reaching a hitherto unseen level of complexity.

To date, no unifying theory nor systematic design methods, techniques and tools exist for such systems.
Individual (mechanical, electrical, network or software) engineering disciplines only offer partial solutions.

Multi-paradigm Modelling (MPM) proposes to model every part and aspect of such complex systems explicitly, at the most appropriate level(s) of abstraction, using the most appropriate modelling formalism(s).
This includes the explicit modelling of the often complex engineering workflows.

Modular modelling language engineering, including model transformation, and the study of modelling language semantics, are used to realize MPM. MPM is seen as an effective answer to the challenges of designing CPS.

This presentation introduces a vision of complex CPS, in particular in the context of Industry 4.0.
The causes of complexity of such systems and some of the challenges of their collaborative development are introduced, as well as possible multi-paradigm modelling solutions such as (in-)consistency management and co-simulation.

Abstract
Philosophers have been talking about metaphysics since Aristotle. Logicians have used metalanguages for 80 years. And, in the last 50 years, computer scientists have produced metaobjects, metaclasses and metamodels. “Going meta” is now even part of the popular culture. What is this all about?

It is about the incredibly powerful human ability to reflect on what we are doing. Bringing this capability to our modeling languages, we can create languages able to express their own definitions. But, with real semantic formalization, we also open up the possibility of creating tools that can reflect on the very models they are being used to create. What might this mean for the next generation of modeling languages and tools?

In this talk we will go meta, reflect on reflection and try to figure it out.

Abstract
Although the notions of software components and component-based systems has been discussed since the earliest days of software engineering, it was the broader adoption of the object paradigm in the late 80’s and early 90’s that provided the critical impetus for research into this domain. Specifically, object-oriented programming provided an alternative to the traditional algorithm-driven model of software by representing a software program primarily as a network of collaborating specialized modules (i.e., objects). In this paradigm, it was natural to characterize components as “black boxes” characterized by their input-output behaviours – a model that is strongly reminiscent of similar models in other forms of engineering (e.g., control, electrical, mechanical). This in turn has led to a number of new theoretical models of computing that exploit the relative simplicity of this representation.

However, most of component-based theories overlook one fundamental difference between software and traditional engineering media: that all software requires an “engine” for it to work. This engine is ultimately rooted in hardware, but, there can be numerous layers between the hardware and the components, each one presenting a realizing its own engine to the software it supports. As explained in this talk, the influence of these oft-neglected platform layers can have a fundamental impact on the design and operation of a component-based software system. A closer examination of the interrelationship between a component and its underlying platforms (NB: in practice there is never just one platform!) reveals what may be a surprising level of complexity. In this talk, we outline how component-based models of computing must be refined to account for these effects to ensure that software component systems fulfill their functional and engineering requirements. This, in turn, leads to further refinements including, notably, the crucial notions of software layering and platform independence.