John Fitzgerald’s research while affiliated with Newcastle University and other places

Businesses, particularly small and medium-sized enterprises, aiming to start up in Model-Based Design (MBD) face difficult choices from a wide range of methods, notations and tools before making the significant investments in planning, procurement and training necessary to deploy new approaches successfully. In the development of Cyber-Physical Systems (CPSs) this is exacerbated by the diversity of formalisms covering computation, physical and human processes. In this paper, we propose the use of a cloud-enabled and open collaboration platform that allows businesses to offer models, tools and other assets, and permits others to access these on a pay-per-use basis as a means of lowering barriers to the adoption of MBD technology, and to promote experimentation in a sandbox environment.

Smart Cyber-Physical Systems (sCPS) are a novel kind of Cyber- Physical System engineered to take advantage of large-scale cooperation between devices, users and environment to achieve added value in the face of uncertainty and changing environments. Examples of sCPS include modern traffic systems, Industry 4.0 systems, systems for smart buildings, and smart energy grids. The uniting aspect of all these systems is that to achieve their high level of intelligence, adaptivity and ability to optimize and learn, they rely heavily on software. This makes them software-intensive systems, where software becomes their most complex part. Engineering sCPS thus becomes a recognized software engineering discipline, which, due to specifics of sCPS, can only partially rely on the existing body of knowledge in software engineering. In fact, it turns out that many of the traditional approaches to architecture modeling and software development fall short in their ability to cope with the high dynamicity and uncertainty of sCPS. This calls for innovative approaches that jointly reflect and address the specifics of such systems. This paper maps the discussions and results of the Fourth International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS 2018), which focuses on challenges and promising solutions in the area of software engineering for sCPS.

Ensuring the dependability of Cyber-Physical Systems (CPSs) poses challenges for model-based engineering, stemming from the semantic heterogeneity of the models of computational, physical and human processes, and from the range of stakeholders involved. We argue that delivering such dependability requires a marriage of multi-disciplinary models developed during design with models derived from real operational data. Assets developed during design thus become the basis of a learning digital twin, able to support decision making both in redesign and in responsive operation. Starting from an open integrated toolchain leveraging formal models for CPS design, we consider the extension of this concept towards digital twins. A small example inspired by agricultural robotics illustrates some of the opportunities for research and innovation in delivering digital twins that contribute to dependability.

Smart Cyber-Physical Systems (sCPS) are a novel kind of Cyber- Physical Systems engineered to take advantage of large-scale cooperation between devices, users and environment to achieve added value in face of uncertainty and various situations in their environment. Examples of sCPS include modern traffic systems, Industry 4.0 systems, systems for smart-buildings, smart energy grids, etc. The uniting aspect of all these systems is that to achieve their high-level of intelligence, adaptivity and ability to optimize and learn, they heavily rely on software. This makes them software-intensive systems, where software becomes their most complex part. Engineering sCPS thus becomes a recognized software engineering discipline, which however, due to specifics of sCPS, can only partially rely on the existing body of knowledge in software engineering. In fact, it turns out that many of the traditional approaches to architecture modeling and software development fail to cope with the high dynamicity and uncertainty of sCPS. This calls for innovative approaches that jointly reflect and address the specifics of such systems. This paper maps the discussions and results of the Third International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS 2017), which specifically focuses on challenges and promising solutions in the area of software engineering for sCPS.

Smart Cyber-Physical Systems (sCPS) are a novel kind of Cyber- Physical Systems engineered to take advantage of large-scale cooperation between devices, users and environment to achieve added value in face of uncertainty and various situations in their environment. Examples of sCPS include modern traffic systems, Industry 4.0 systems, systems for smart-buildings, smart energy grids, etc. The uniting aspect of all these systems is that to achieve their high-level of intelligence, adaptivity and ability to optimize and learn, they heavily rely on software. This makes them software-intensive systems, where software becomes their most complex part. Engineering sCPS thus becomes a recognized software engineering discipline, which however, due to specifics of sCPS, can only partially rely on the existing body of knowledge in software engineering. In fact, it turns out that many of the traditional approaches to architecture modeling and software development fail to cope with the high dynamicity and uncertainty of sCPS. This calls for innovative approaches that jointly reflect and address the specifics of such systems. This paper maps the discussions and results of the Third International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS 2017), which specifically focuses on challenges and promising solutions in the area of software engineering for sCPS.

Cyber-Physical Systems (CPSs) [1] connect the real world to software systems through a network of sensors and actuators in which physical and logical components interact in complex ways. There is a diverse range of application domains [2], including health [3], energy [4], transport [5], autonomous vehicles [6] and robotics [7]; and many of these include safety critical requirements [8]. Such systems are, by definition, characterised by both discrete and continuous components. The development and verification processes must, therefore, incorporate and integrate discrete and continuous models.

The languages used to express specifications, models and programs have much in common. However, in this paper we argue that because they serve different purposes, real care should be taken to distinguish them during development. Rather than seeking unification at the language level, we would recommend exploiting intersections between them where they arise. The main contribution of this paper is to point out the necessary differences and to offer evidence of situations in which common ground can be reached.