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Complex assistive systems providing human behavior support independent of the age or abilities of users are broadly used in a variety of domains including automotive, production, aviation, or medicine. Current research lacks a common understanding of which architectural components are needed to create assistive systems that use models at runtime. Existing descriptions of architectural components are focused on particular domains, consider only some parts of an assistive system, or do not consider models at runtime. We have analyzed common functional requirements for such systems to be able to propose a set of reusable components, which have to be considered when creating assistive systems that use models. Such components constitute a reference architecture that we propose within this paper. To validate the proposed architecture, we have expressed the architectures of two assistive systems from different domains, namely assistance for elderly people and assistance for operators in smart manufacturing in terms of compliance with such architecture. The proposed reference architecture will facilitate the creation of future assistive systems. [ABSTRACT FROM AUTHOR]

The methodology under the term model-based software engineering (MBSE) gained importance already around 20 years ago, after the publication of the Model-Driven Architecture (MDA) initiative by the Object Management Group (OMG). This development methodology continues to evolve, giving rise to recent proposals such as low-code or no-code. Something that has not changed, as recent surveys point out, is the need for powerful testing approaches and tools for these new methodologies. In MBSE, test inputs are models, so it is key to have frameworks for model generation. However, the main shortcomings of existing model-generation frameworks are their performance limitations and the need for domain-specific knowledge, which seriously hampers their industrial adoption. In this paper, we present the Yekta low-code framework that allows to generate models in a simple way through the application of metaheuristic algorithms.

To obtain more financial freedom, universities and especially their chairs and institutes have to establish a well functioning and reliable financial management and accounting system. Currently, chairs have different technical solutions for these systems, each of which must react individually to external changes and require a high effort to adapt their reports. Thus, they rely on either commercial accounting software, which is not tailored to their specific needs, or standard spreadsheet software making use of complex sheets and cross-references which are error-prone and hard to adapt. We have used domain models and code synthesis methods to create an enterprise information system. This paper shows how models reflect user requirements, evolve with changing requirements and how they impact an agile, model-driven engineering process. The resulting system simplifies the planning of financial management and accounting by university chairs. [ABSTRACT FROM AUTHOR]

Adaptive systems manage and regulate the behavior of devices or other systems using control loops to automatically adjust the value of some measured variables to equal the value of a desired set-point. These systems normally interact with physical parts or operate in physical environments, where uncertainty is unavoidable. Traditional approaches to manage that uncertainty use either robust control algorithms that consider bounded variations of the uncertain variables and worst-case scenarios or adaptive control methods that estimate the parameters and change the control laws accordingly. In this article, we propose to include the sources of uncertainty in the system models as first-class entities using random variables to simulate adaptive and control systems more faithfully, including not only the use of random variables to represent and operate with uncertain values but also to represent decisions based on their comparisons. Two exemplar systems are used to illustrate and validate our proposal. [ABSTRACT FROM AUTHOR]

Conceptual models are an effective and unparalleled means to communicate complicated information with a broad variety of stakeholders in a short period of time. However, in practice, conceptual models often vary in clarity, employed features, communicated content, and overall quality. This potentially impacts model comprehension to a point where models are factually useless. To counter this, guidelines to create “good” conceptual models have been suggested. However, these guidelines are often abstract, hard to operationalize in different modeling languages, partly overlap, or even contradict one another. In addition, no comparative study of proposed guidelines exists so far. This issue is exacerbated as no established metrics to measure or estimate model comprehension for a given conceptual model exist. In this article, we present the results of a literature survey investigating 109 publications in the field and discuss metrics to measure model comprehension, their quantification, and their empirical substantiation. Results show that albeit several concrete quantifiable metrics and guidelines have been proposed, concrete evaluative recommendations are largely missing. Moreover, some suggested guidelines are contradictory, and few metrics exist that allow instantiating common frameworks for model quality in a specific way.

To gain long-term knowledge on product and process quality the sustainable collection, processing and storage of sensor data is an essential requirement. Whereas the application of the FAIR principles as a guideline to provide all collected data with rich metadata that allow for finding, accessing, interoperating with and reusing the data effectively is constantly growing within science and research, the usage in an industrial scenario is largely unexplored. Today, collected industrial data is often stored in arbitrary data formats without appropriate metadata describing the data content and is therefore lost for future reuse because crucial information on how to find, access and interoperate with the data is missing. Moreover, insufficiently described or missing data can lead to wrong decisions. In order to implement the FAIR principles for industrial sensor data, this article derives the major deficits and challenges of making industrial sensor data FAIR. Furthermore, the authors propose a three-layer architecture of FAIR sensor services, which handles the discussed challenges, to acquire FAIR sensor data at the time of measurement. The conceptual draft is evaluated by a prototypical implementation of a FAIR sensor service.

Software product line engineering emerged as an effective approach for the development of families of software‐intensive systems in several industries. Although its use has been widely discussed and researched, there are still several open challenges for its industrial adoption and application. One of these is how to efficiently develop and reuse shared software artifacts, which have dependencies on the underlying electrical and hardware systems of products in a family. In this work, we report on our experience in tackling such a challenge in the railway industry and present a model‐based approach for the automatic generation of test scripts for product variants in software product lines. The proposed approach is the result of an effort leveraging the experiences and results from the technology transfer activities with our industrial partner Alstom SA in Sweden. We applied and evaluated the proposed approach on the Aventra software product line from Alstom SA. The evaluation showed that the proposed approach mitigates the development effort, development time, and consistency drawbacks associated with the traditional, manual creation of test scripts. We performed an online survey involving 37 engineers from Alstom SA for collecting feedback on the approach. The result of the survey further confirms the aforementioned benefits. [ABSTRACT FROM AUTHOR]

With the increasing computing and networking capabilities, IoT devices and edge gateways have become part of a larger IoT–edge–cloud computing continuum, where processing and storage tasks are distributed across the whole network hierarchy, not concentrated only in the cloud. At the same time, this also introduced continuous delivery practices to the development of software components for network-connected gateways and sensing/actuating nodes. These devices are placed on end users' premises and are characterized by continuously changing cyber-physical contexts, forcing software developers to maintain multiple application versions and frequently redeploy them on a distributed fleet of devices with respect to their current contexts. Doing this correctly and efficiently goes beyond manual capabilities and requires an intelligent and reliable automated solution. This paper describes a model-based approach to automatically assigning multiple software deployment plans to hundreds of edge gateways and connected IoT devices implemented in collaboration with a smart healthcare application provider. From a platform-specific model of an existing edge computing platform, we extract a platform-independent model that describes a list of target devices and a pool of available deployment plans. Next, we use constraint solving to automatically assign deployment plans to devices at once with respect to their specific contexts. The result is transformed back into the platform-specific model and includes a suitable deployment plan for each device, which is then consumed by our engine to deploy software components not only on edge gateways but also on their downstream IoT devices with constrained resources and connectivity. We validate the approach with a fleet deployment prototype integrated into a DevOps toolchain used by the partner application provider. Initial experiments demonstrate the viability of the approach and its usefulness in supporting DevOps for edge and IoT software development. [ABSTRACT FROM AUTHOR]

Blockchain technology promises to spark a real revolution. One of most important concepts associated with this technology is smart contracts, which enable the automatic execution of agreements and augur a world without intermediaries. The conditions and rules of “contracts” are established in a computer codes and trust is enforced by consensus among the participants. One relevant feature associated with smart contract is the immutability property, which establishes the non-alteration of blockchain network data after the clauses of the contract are been approved by all parties or entities involved. For this reason, smart contract development requires more effort and care than the development of other common programs. They require systematic mechanisms to collect requirements and functional specifications. In addition, it is necessary to verify and validate the agreed functionality and the implemented code before they are deployed in the blockchain platform. This article presents a systematic literature review of primary studies in the field of Software Development Life Cycle, focusing on model-based software design and testing in the blockchain domain of smart contracts. This research aims to identify gaps and/or opportunities for further research. After carried out this review, it was observed that no clear methodology exists for evaluating and validating the quality either of this software or the overall development process. This means that software developers may implement smart contract code in which bugs and serious security vulnerabilities appear when the software is delivered to their customers.

Sustainable collection, processing and storage of sensor data in industrial context are essential requirements for gaining long-term knowledge on product and process quality. Today, collected data is often stored in arbitrary data formats without appropriate metadata describing the data content and is therefore lost for future reuse because crucial information on how to find, access and interoperate with the data is missing. Moreover, insufficiently described or missing data can lead to wrong decisions. In a scientific context, the FAIR data principles are an emerging design guideline to provide all collected data with rich metadata that allow for finding, accessing, interoperating with and reusing the data effectively later. This publication proposes an approach to implement the FAIR principles for industrial sensor data. It analyses major deficits and challenges of making industrial sensor data FAIR and outlines a system architecture for acquisition of FAIR industrial sensor data, called FAIR sensor services.

Adapting the user interface of a software system to the requirements of the context of use continues to be a major challenge, particularly when users become more demanding in terms of adaptation quality. A considerable number of methods have, over the past three decades, provided some form of modelling with which to support user interface adaptation. There is, however, a crucial issue as regards in analysing the concepts, the underlying knowledge, and the user experience afforded by these methods as regards comparing their benefits and shortcomings. These methods are so numerous that positioning a new method in the state of the art is challenging. This paper, therefore, defines a conceptual reference framework for intelligent user interface adaptation containing a set of conceptual adaptation properties that are useful for model-based user interface adaptation. The objective of this set of properties is to understand any method, to compare various methods and to generate new ideas for adaptation. We also analyse the opportunities that machine learning techniques could provide for data processing and analysis in this context, and identify some open challenges in order to guarantee an appropriate user experience for end-users. The relevant literature and our experience in research and industrial collaboration have been used as the basis on which to propose future directions in which these challenges can be addressed. [ABSTRACT FROM AUTHOR]

The development of Human Robot Collaborative (HRC) systems faces many challenges. First, HRC systems should be adaptable and re-configurable to support fast production changes. However, in the development of HRC applications safety considerations are of paramount importance, as much as classical activities such as task programming and deployment. Hence, the reconfiguration and reprogramming of executing tasks might be necessary also to fulfill the desired safety requirements. Model-based software engineering is a suitable means for agile task programming and reconfiguration. We propose a model-based design-to-deployment toolchain that simplifies the routine of updating or modifying tasks. This toolchain relies on (i) UML profiles for quick model design, (ii) formal verification for exhaustive search for unsafe situations (caused by intended or unintended human behavior) within the model, and (iii) trans-coding tools for automating the development process. The toolchain has been evaluated on a few realistic case studies. In this paper, we show a couple of them to illustrate the applicability of the approach. [ABSTRACT FROM AUTHOR]

Modeling is requiring increasingly larger efforts while becoming indispensable given the complexity of the problems we are solving. Modelers face high cognitive load to understand a multitude of complex abstractions and their relationships. There is an urgent need to better support tool builders to ultimately provide modelers with intelligent modeling assistance that learns from previous modeling experiences, automatically derives modeling knowledge, and provides context-aware assistance. However, current intelligent modeling assistants (IMAs) lack adaptability and flexibility for tool builders, and do not facilitate understanding the differences and commonalities of IMAs for modelers. Such a patchwork of limited IMAs is a lost opportunity to provide modelers with better support for the creative and rigorous aspects of software engineering. In this expert voice, we present a conceptual reference framework (RF-IMA) and its properties to identify the foundations for intelligent modeling assistance. For tool builders, RF-IMA aims to help build IMAs more systematically. For modelers, RF-IMA aims to facilitate comprehension, comparison, and integration of IMAs, and ultimately to provide more intelligent support. We envision a momentum in the modeling community that leads to the implementation of RF-IMA and consequently future IMAs. We identify open challenges that need to be addressed to realize the opportunities provided by intelligent modeling assistance. [ABSTRACT FROM AUTHOR]

Although Model-Based Software Engineering (MBE) is a widely accepted Software Engineering (SE) discipline, no agreed-upon core set of concepts and practices (i.e., a Body of Knowledge) has been defined for it yet. With the goals of characterizing the contents of the MBE discipline, promoting a global consistent view of it, clarifying its scope with regard to other SE disciplines, and defining a foundation for the development of educational curricula on MBE, this paper proposes the contents for a Body of Knowledge for MBE. We also describe the methodology that we have used to come up with the proposed list of contents, as well as the results of a survey study that we conducted to sound out the opinion of the community on the importance of the proposed topics and their level of coverage in the existing SE curricula. [ABSTRACT FROM AUTHOR]

The capability of UML profiles to serve as annotation mechanism has been recognized in both research and industry. Today’s modeling tools offer profiles specific to platforms, such as Java, as they facilitate model-based engineering approaches. However, considering the large number of possible annotations in Java, manually developing the corresponding profiles would only be achievable by huge development and maintenance efforts. Thus, leveraging annotation-based modeling requires an automated approach capable of generating platform-specific profiles from Java libraries. To address this challenge, we present the fully automated transformation chain realized by Jump, thereby continuing existing mapping efforts between Java and UML by emphasizing on annotations and profiles. The evaluation of Jump shows that it scales for large Java libraries and generates profiles of equal or even improved quality compared to profiles currently used in practice. Furthermore, we demonstrate the practical value of Jump by contributing profiles that facilitate reverse engineering and forward engineering processes for the Java platform by applying it to a modernization scenario. [ABSTRACT FROM AUTHOR]

The continuously increasing complexity of modern and sustainable power and energy systems leads to a wide range of solutions developed by industry and academia. To manage such complex system-of-systems, proper engineering and validation approaches, methods, concepts, and corresponding tools are necessary. The Smart Grid Architecture Model (SGAM), an approach that has been developed during the last couple of years, provides a very good and structured basis for the design, development, and validation of new solutions and technologies. This review therefore provides a comprehensive overview of the state-of-the-art and related work for the theory, distribution, and use of the aforementioned architectural concept. The article itself provides an overview of the overall method and introduces the theoretical fundamentals behind this approach. Its usage is demonstrated in several European and national research and development projects. Finally, an outlook about future trends, potential adaptations, and extensions is provided as well.

This paper summarizes the information technology-related research findings after 5 years with the INTROducing Mental health through Adaptive Technology project. The aim was to improve mental healthcare by introducing new technologies for adaptive interventions in mental healthcare through interdisciplinary research and development. We focus on the challenges related to internet-delivered psychological treatments, emphasising artificial intelligence, human-computer interaction, and software engineering. We present the main research findings, the developed artefacts, and lessons learned from the project before outlining directions for future research. The main findings from this project are encapsulated in a reference architecture that is used for establishing an infrastructure for adaptive internet-delivered psychological treatment systems in clinical contexts. The infrastructure is developed by introducing an interdisciplinary design and development process inspired by domain-driven design, user-centred design, and the person based approach for intervention design. The process aligns the software development with the intervention design and illustrates their mutual dependencies. Finally, we present software artefacts produced within the project and discuss how they are related to the proposed reference architecture. Our results indicate that the proposed development process, the reference architecture and the produced software can be practical means of designing adaptive mental health care treatments in correspondence with the patients' needs and preferences. In summary, we have created the initial version of an information technology infrastructure to support the development and deployment of Internet-delivered mental health interventions with inherent support for data sharing, data analysis, reusability of treatment content, and adaptation of intervention based on user needs and preferences., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

Abstract: The cost of correcting errors in the design of an embedded system’s hardware components can be higher than for its software components, making it important to test as early as possible. Testing hardware components before they are implemented involves verifying the design through either formal or more commonly, simulation-based functional verification. Performing functional verification of a hardware design requires software-based simulators and verification testbenches. However, the increasing complexity of embedded systems is contributing to testbenches that are progressively more difficult to understand, maintain, extend and reuse across projects. This paper presents an aspect-oriented domain-specific modelling language for the e hardware verification language that can be used as part of a model-based software engineering process. The modelling language is designed to produce well modularised models from which e code can be generated, thereby improving engineers ability to develop testbenches that can be more easily maintained, adapted and reused. We demonstrate the suitability of the modelling language through its application to a representative testbench from the automotive semiconductor industry. [Copyright &y& Elsevier]

The Model Driven Architecture (MDA) is an approach to develop software based on different models. There are separate models for the business logic and for platform specific details. Moreover, code can be generated automatically from these models. This makes transforma- tions a core technology for MDA and for model-based software engineering approaches in general. Query/View/Transformation (QVT) is the transformation technology recently proposed for this purpose by the OMG. Triple Graph Grammars (TGGs) are another transformation technology proposed in the mid-nineties, used for example in the FUJABA CASE tool. In contrast to many other transformation technologies, both QVT and TGGs declaratively define the relation between two models. With this definition, a transformation engine can execute a transformation in either direction and, based on the same definition, can also propagate changes from one model to the other. In this paper, we compare the concepts of the declarative languages of QVT and TGGs. It turns out that TGGs and declarative QVT have many concepts in common. In fact, QVT-Core can be mapped to TGGs. We show that QVT-Core can be implemented by transforming QVT-Core mappings to TGG rules, which can then be executed by a TGG transformation engine that performs the actual QVT transformation. Furthermore, we discuss an approach for mapping QVT-Relations to TGGs. Based on the semantics of TGGs, we clarify semantic gaps that we identified in the declarative languages of QVT and, furthermore, we show how TGGs can benefit from the concepts of QVT. [ABSTRACT FROM AUTHOR]