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International audience; The advent of complex Cyber-Physical Systems (CPSs) creates the need for more efficient engineering processes. Recently, DevOps promoted the idea of considering a closer continuous integration between system development (including its design) and operational deployment. Despite their use being still currently limited, Artificial Intelligence (AI) techniques are suitable candidates for improving such system engineering activities (cf. AIOps). In this context, AIDOaRT is a large European collaborative project that aims at providing AI-augmented automation capabilities to better support the modelling, coding, testing, monitoring, and continuous development of CPSs. The project proposes to combine Model Driven Engineering principles and techniques with AI-enhanced methods and tools for engineering more trustable CPSs. The resulting framework will 1) enable the dynamic observation and analysis of system data collected at both runtime and design time and 2) provide dedicated AI-augmented solutions that will then be validated in concrete industrial cases. This paper describes the main research objectives and underlying paradigms of the AIDOaRt project. It also introduces the conceptual architecture and proposed approach of the AIDOaRt overall solution. Finally, it reports on the actual project practices and discusses the current results and future plans.

Emerging and future applications of robotic systems pose unique self-adaptation challenges. To support the research needed to address these challenges, we provide an extensible repository of robotic mission adaptation exemplars. Co-designed with robotic application stakeholders including researchers, developers, operators, and end-users, our repository captures key sources of uncertainty, adaptation concerns, and other distinguishing characteristics of such applications. An online form enables external parties to supply new exemplars for curation and inclusion into the repository. We envisage that our RoboMAX repository will enable the development, evaluation, and comparison of much-needed self-adaptation approaches for the robotic systems domain.

DevOps and Model Driven Engineering (MDE) provide differently skilled IT stakeholders with methodologies and tools for organizing and automating continuous software engineering activities-from development to operations, and using models as key engineering artifacts, respectively. Both DevOps and MDE aim at shortening the development life-cycle, dealing with complexity, and improve software process and product quality. The integration of DevOps and MDE principles and practices in low-code engineering platforms (LCEP) are gaining attention by the research community. However, at the same time, new requirements are upcoming for DevOps and MDE as LCEPs are often used by non-technical users, to deliver fully functional software. This is in particular challenging for current DevOps processes, which are mostly considered on the technological level, and thus, excluding most of the current LCEP users. The systematic use of models and modeling to lowering the learning curve of DevOps processes and platforms seems beneficial to make them also accessible for non-technical users. In this paper, we introduce DevOpsML, a conceptual framework for modeling and combining DevOps processes and platforms. Tools along with their interfaces and capabilities are the building blocks of DevOps platform configurations, which can be mapped to software engineering processes of arbitrary complexity. We show our initial endeavors on DevOpsML and present a research roadmap how to employ the resulting DevOpsML framework for different use cases.

Domain-specific languages (DSLs) are popular for many reasons, such as increasing productivity for developers and improving communication with domain experts. Both textual and graphical DSLs are viable solutions with complementary pros and cons: while graphical DSLs shorten the learning curve and facilitate documentation and communication, textual DSLs aim at higher productivity thanks to more efficient editor functionalities. This paper presents the industrial experience on the adoption of a hybrid approach combining an existing textual DSL with a read-only graphical state machine representation (visualization), equipped with a selective abstraction functionality that offers user-specific, highly configurable views on states and transitions. Our approach is the result of an evolutionary process to improve the modelling experience, relying on frequent user feedback. We argue that a well-tailored visualization is a suitable way to shorten the learning curve and ease the adoption of model-driven approaches in industrial settings.

Computer Aided Engineering Exchange (CAEX) is one of the most promising standards when it comes to data exchange between engineering tools in the production system automation domain. This is also reflected by the current emergence of AutomationML (AML), which uses CAEX as its core representation language. However, with the increasing use of CAEX, important language engineering challenges arise. One of these challenges is the customization of CAEX for its usage in superior standards, such as AML, which requires the precise specification of the language including the formalization and validation of additional usage rules. Another highly topical challenge is the ongoing evolution of CAEX as has recently happened with the transition from version 2.15 to version 3.0. Further challenges include the provisioning of editing facilities and visualizations of CAEX documents such that they can be inspected and modified by engineers, and the development of transformations from and to CAEX such that different engineering artifacts can be exchanged via CAEX. In this paper, we take a language engineering point of view and present a model-driven engineering (MDE) workbench for CAEX that allows to address these and other challenges. In particular, we present how CAEX can be formulated in a model-based framework, which allows the application of MDE techniques, such as model validation, migration, editing, visualization, and transformation techniques, to solve a diverse set of language engineering challenges experienced for CAEX. We give an overview of the developed workbench and illustrate its benefits with a focus on customizing CAEX for AML and evolving CAEX documents from version 2.15 to 3.0.

Context awareness is a first-class attribute of today software systems. Indeed, many applications need to be aware of their context in order to adapt their structure and behavior for offering the best quality of service even in case the software and hardware resources are limited. Modeling the context, its evolution, and its influence on the services provided by (possibly resource constrained) applications are becoming primary activities throughout the whole software life cycle, although it is still difficult to capture the multidimensional nature of context. We propose a framework for modeling and reasoning on the context and its evolution along multiple dimensions. Our approach enables (1) the representation of dependencies among heterogeneous context attributes through a formally defined semantics for attribute composition and (2) the stochastic analysis of context evolution. As a result, context can be part of a model-based software development process, and multidimensional context analysis can be used for different purposes, such as non-functional analysis. We demonstrate how certain types of analysis, not feasible with context-agnostic approaches, are enabled in our framework by explicitly representing the interplay between context evolution and non-functional attributes. Such analyses allow the identification of critical aspects or design errors that may not emerge without jointly taking into account multiple context attributes. The framework is shown at work on a case study in the eHealth domain.

International audience; Context: Managing Non-Functional Requirements (NFRs) in software projects is challenging, and projects that adopt Model-Driven Development (MDD) are no exception. Although several methods and techniques have been proposed to face this challenge, there is still little evidence on how NFRs are handled in MDD by practitioners. Knowing more about the state of the practice may help researchers to steer their research and practitioners to improve their daily work. Objective: In this paper, we present our findings from an interview-based survey conducted with practitioners working in 18 different companies from 6 European countries. From a practitioner's point of view, the paper shows what barriers and benefits the management of NFRs as part of the MDD process can bring to companies, how NFRs are supported by MDD approaches, and which strategies are followed when (some) types of NFRs are not supported by MDD approaches. Results: Our study shows that practitioners perceive MDD adoption as a complex process with little to no tool support for NFRs, reporting productivity and maintainability as the types of NFRs expected to be supported when MDD is adopted. But in general, companies adapt MDD to deal with NFRs. When NFRs are not supported, the generated code is sometimes changed manually, thus compromising the maintainability of the software developed. However, the interviewed practitioners claim that the benefits of using MDD outweight the extra effort required by these manual adaptations. Conclusion: Overall, the results indicate that it is important for practitioners to handle NFRs in MDD, but further research is necessary in order to lower the barrier for supporting a broad spectrum of NFRs with MDD. Still, much conceptual and tool implementation work seems to be necessary to lower the barrier of integrating the broad spectrum of NFRs in practice.

AutomationML (AML) is an emerging standard in the automation domain to represent and exchange artifacts between heterogeneous engineering tools used in different disciplines, such as mechanical and electrical engineering. The Systems Modeling Language (SysML) is a modeling standard influenced by software modeling languages, such as UML, typically adopted in the early phases of engineering processes. This paper investigates commonalities and differences of the structural modeling parts of AML (CAEX) and SysML (block diagrams) in support of establishing tool-independent interoperability. This support for cross-disciplinary modeling is facilitated by a bridge between AML and SysML built on model-driven interoperability techniques. We demonstrate the interoperability between AML and SysML with a case study concerning a lab-sized production system.

Modern Cyber-Physical Systems (CPS) and Internet of Things (IoT) systems consist of both loosely and tightly interactions among various resources in IoT networks, edge servers and cloud data centers. These elements are being built atop virtualization layers and deployed in both edge and cloud infrastructures. They also deal with a lot of data through the interconnection of different types of networks and services. Therefore, several new types of uncertainties are emerging, such as data, actuation, and elasticity uncertainties. This triggers several challenges for testing uncertainty in such systems. However, there is a lack of novel ways to model and prepare the right infrastructural elements covering requirements for testing emerging uncertainties. In this paper, first we present techniques for modeling CPS/IoT Systems and their uncertainties to be tested. Second, we introduce techniques for determining and generating deployment configuration for testing in different IoT and cloud infrastructures. We illustrate our work with a real-world use case for monitoring and analysis of Base Transceiver Stations.

Variability modeling is an emerging topic in the general field of systems engineering and, with current trends such as Industrie 4.0, it gains more and more interest in the domain of production systems. Therefore, it is not sufficient to describe systems in several specific cases, but instead families of systems have to be used. In this paper we introduce a role class library for AutomationML to explicitly represent variability. This allows to exchange not only system descriptions but also system family descriptions. We argue for a light-weight extension of AutomationML. The variability-based modeling approach is based on cardinalities, which is a well-known concept from conceptual modeling and feature modeling. Furthermore, we also show how instantiations of variability models can be validated by our EMF-based AutomationML workbench.

In multi-disciplinary engineering (MDE) projects, information models play an important role as inputs to and outputs of engineering processes. In MDE projects, engineers collaborate from various disciplines, such as mechanical, electrical, and software engineering. These disciplines use general-purpose and domain-specific models in their engineering context. Important challenges include model synchronization and model quality assurance (MQA) that are covered insufficiently in current MDE practices. This chapter focuses on the needs and approaches for MQA in MDE environments. We address the following two research questions (RQs): The first RQ focuses on investigating needs and expected capabilities that are required for a systematic review process that focuses on changes in MDE design models (RQ-MQA1). The second RQ focuses on how to extend a standard modeling language for MDE, such as the AutomationML, to address needs for storing process-relevant attributes in the context of quality assurance and review process support (RQ-MQA2). This chapter presents concepts and an initial evaluation of MQA approaches in the context of selected MDE processes, i.e., the addition, change, or removal of a component in an engineering discipline and an impact analysis on the integrated plant model. Main results are that (a) an adapted review process helps to systematically drive model reviews for MDE and (b) the standardized language description of AutomationML can be extended with process-related attributes that are useful for quality assurance and reviewing.

To engineer large, complex, and interdisciplinary systems, modeling is considered as the universal technique to understand and simplify reality through abstraction, and thus, models are in the center as the most important artifacts throughout interdisciplinary activities within model-driven engineering processes. Model-Driven Systems Engineering (MDSE) is a systems engineering paradigm that promotes the systematic adoption of models throughout the engineering process by identifying and integrating appropriate concepts, languages, techniques, and tools. This chapter discusses current advances as well as challenges towards the adoption of model-driven approaches in cyber-physical production systems (CPPS) engineering. In particular, we discuss how modeling standards, modeling languages, and model transformations are employed to support current systems engineering processes in the CPPS domain, and we show their integration and application based on a case study concerning a lab-sized production system. The major outcome of this case study is the realization of an automated engineering tool chain, including the languages SysML, AML, and PMIF, to perform early design and validation.

Today's cyber-physical systems (CPS) span IoT and cloud-based datacenter infrastructures, which are highly heterogeneous with various types of uncertainty. Thus, testing uncertainties in these CPS is a challenging and multidisciplinary activity. We need several tools for modeling, deployment, control, and analytics to test and evaluate uncertainties for different configurations of the same CPS. In this paper, we explain why using state-of-the art model-driven engineering (MDE) and model-based testing (MBT) tools is not adequate for testing uncertainties of CPS in IoT Cloud infrastructures. We discus how to combine them with techniques for elastic execution to dynamically provision both CPS under test and testing utilities to perform tests in various IoT Cloud infrastructures.