In 2017 and 2018, two events were held-in Marburg, Germany, and San Vigilio di Marebbe, Italy, respectively-focusing on an analysis of the state of research, state of practice, and state of the art in model-driven engineering (MDE). The events brought together experts from industry, academia, and the open-source community to assess what has changed in research in MDE over the last 10 years, what challenges remain, and what new challenges have arisen. This article reports on the results of those meetings, and presents a set of grand challenges that emerged from discussions and synthesis. These challenges could lead to research initiatives for the community going forward. Keywords Model-driven engineering • Grand challenge • Research roadmap 1 Introduction The field of model-driven engineering [1] (MDE) has evolved substantially from the earliest work on UML in the 1990s, through to seminal research on metamodeling, model transformation, and model management in the earlyto-mid-2000s. MDE has made incredible contributions to leverage abstraction and automation in almost every area of software and systems development and analysis. In many domains, including railway systems, automotive, business process engineering, and embedded systems, models are key to success in modern software engineering processes. How-Communicated by Bernhard Rumpe.

We propose a comprehensive framework for adaptivity of service-based applications, which exploits the concept of process fragments as a way to model reusable process knowledge and to allow for the dynamic, incremental, contextaware composition of such fragments into adaptable servicebased applications. The framework provides a set of adaptation mechanisms that, combined through adaptation strategies, are able to solve complex adaptation problems. An implementation of the proposed solution is presented and evaluated on a realworld scenario from the logistics domain.

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In the last decade, many approaches to automated service composition have been proposed. However, most of them do not fully exploit the opportunities offered by the Internet of Services (IoS). In this article, we focus on the dynamicity of the execution environment, that is, any change occurring at run-time that might affect the system, such as changes in service availability, service behavior, or characteristics of the execution context. We indicate that any IoS-based application strongly requires a composition framework that supports for the automation of all the phases of the composition life cycle, from requirements derivation, to synthesis, deployment and execution. Our solution to this ambitious problem is an AI planning-based composition framework that features abstract composition requirements and context-awareness. In the proposed approach most human-dependent tasks can be accomplished at design time and the few human intervention required at run time do not affect the system execution. To demonstrate our approach in action and evaluate it, we exploit the ASTRO-CAptEvo framework, simulating the operation of a fully automated IoS-based car logistics scenario in the Bremerhaven harbor.

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