Keynote Lectures

Abstract
The digital transformation has a major impact on the systems that we are developing in essentially all application domains, including transportation (e.g. smart mobility, autonomous vehicles), manufacturing (e.g. industry 4.0), construction (e.g. smart building, smart cities), and healthcare (e.g. telemedicine, wearable/implantable devices). A key characteristic of the new generation of systems is that software is now the main product differentiator. To succeed, traditional software approaches are no longer adequate. Organizations now need to address a set of new challenges, both from a technical and a business perspective, and adopt new approaches to increase their agility to adapt to constantly evolving environments to deliver solutions faster, with higher quality, and that are adaptable to user needs.
In this context, DevOps and Digital Twins (DT) have emerged over the last two decades as two key approaches for the development and evolution of digital systems in the industry. On one side, DevOps is the prominent approach to improve software processes in terms of productivity and quality. It advocates for continuous improvement through the use of automation and monitoring at all stages of software development and operations, and aims for shorter development cycles, increased frequency of deployment, and more reliable releases. Its adoption by industry leaders (e.g. Amazon, Facebook, Google, and Netflix) has resulted in spectacular progress. On the other side, DT is considered a main technology to monitor and improve systems in the digital era. A DT is a digital representation of an actual system that can be used to systematically monitor, analyse, and improve the system. Both approaches aim at leveraging data and automation to systematically and continuously improve different aspect of systems and processes. They also both heavily rely on different types of models.
In this talk, I will discuss the central role that models play in DevOps and Digital Twins, and more generally in the digital transformation that is currently occurring in the society. I present some of the research projects that we have been conducting in collaboration with industrial partners over the last five years.

Abstract
Modeling is more important than ever but it keeps missing the boat. We can discuss all the reasons why this keeps happening but, for sure, a key one is the lack of flexible and agile modeling environments that maximize the ROI (Return On Investment) of companies adopting modelling practices. Low-code tools are a step in the right direction by streamlining model-driven development processes. But they are limited to classical domains and specific types of business applications. We need a better platform that helps us to quickly show how modelling can be useful in a number of new interdisciplinary scenarios. During the talk we will present BESSER (“BEtter Smart Software fastER”), a new low-code platform we are designing based on the lessons learnt after developing many other modelling tools. BESSER is planned as a long-term open-source project. I will try to convey why BESSER has the right ingredients for becoming a perfect low-code platform. I hope to even be able to convince some of you to join the initiative and use BESSER in your next modelling project.

Abstract
In this talk, we will give both a retrospective on the diverse and complex body of knowledge that focuses on the execution of models, especially for UML-based languages, and a perspective on what model execution may look like in the years to come. While the former is based on a thorough systematic investigation of the body of knowledge, the latter is centrered on previous experiences in both academic and industrial contexts. More specifically, focusing on complex cyber-physical systems with safety constraints, and on the fact that code generators are language-specific, inflexible, and not always reliable, difficult and expensive to certify, customise, and maintain, we outline ORPHEUS, a novel approach for model compilation based on state-of-the-art methods and tools. This kind of approach aims at maximising the profit of using model-based techniques, thus producing high-quality and safe software in a more efficient manner, and accelerating research by providing a unified common ground for researchers and practitioners.