https://www.mdu.se/

Digital twins serve as virtual representations of systems, enabling capabilities such as

intelligent monitoring, real-time control, decision-making, and predictive analytics. The

Asset Administration Shell (AAS) is the pivotal Industry 4.0 standard for digital twin

engineering. In parallel, the Systems Modeling Language version 2 (SysMLv2) has

emerged as the main modeling standard for systems engineering, providing a

formalized and semantically rich approach to system modeling. With its growing

adoption, models developed in the early engineering phases are expected to be widely

available in an interchangeable format, encouraging their reuse in digital twins. Instead

of manually re-creating models for digital twinning, existing system models can be

leveraged.

However, SysMLv2 lacks direct integration with digital twin standards such as AAS,

necessitating a dedicated approach for seamless interoperability between the early

engineering phases and the operation of digital twins.

This paper investigates the conceptual alignment between SysMLv2 and AAS

specifications, examines structural and behavioral modeling aspects, and proposes a

systematic approach to mapping SysMLv2 to AAS, ensuring the automatic generation

of AAS models from SysMLv2 models.

To realize this approach, we employ model-driven engineering techniques leveraging

the Eclipse Modeling Framework and model transformations based on the Query View

Transformation language.

The proposed model transformation incorporates query mechanisms for structured

element extraction, information and structural integrity preservation, ensuring semantic

consistency and seamless data integration between the two investigated standards.

We develop and validate the model transformation following an iterative test-driven

development approach using a set of 24 SysMLv2 examples, sourced from the official

SysMLv2 repository.

The 6th edition of the workshop on Automated and verifiable Software sYstem DEvelopment (ASYDE) provided a forum to share and discuss innovative contributions to research and practice related to novel software engineering approaches to automated and verifiable development of software systems.

A crucial element of Industry 4.0, is the utilization of smart devices that generate log files. Log files are key components containing data on system operations, faults (unexpected glitches or malfunctions), errors (mistakes or incorrect actions), and failures (complete breakdowns or non-functionality). This paper presents a systematic mapping study analyzing research conducted on log files for smart-troubleshooting in Industry 4.0. To the best of our knowledge, this is the study that aims to identify research trends, log file attributes, techniques, and challenges involved in log file analysis for smart-troubleshooting. From an initial set of 941 potentially relevant peer-reviewed publications, 74 primary studies were selected and analyzed using a meticulous data extraction, analysis, and synthesis process. The results of the study demonstrate that the majority of research has focused on developing algorithms for log analysis, with machine learning being the most commonly used approach. The smart-troubleshooting encompasses a range of activities and tools that are essential for collecting failure data generated by diverse interconnected devices, conducting analyses, and aligning them with troubleshooting instructions and software remedies. Moreover, the study identifies the need for further research in the areas of real-time log analysis, anomaly detection, and the integration of log analysis with other Industry 4.0 technologies. In conclusion, our study provides insights into the current state of research in log analysis for smart-troubleshooting in Industry 4.0 and identifies areas for future research. The use of smart devices generating log files in Industry 4.0 highlights the importance of log file analysis for troubleshooting purposes. Further research is needed to address the challenges and opportunities in this field to integrate log analysis with other Industry 4.0 technologies for performing more efficient and effective troubleshooting.

Context: Machine learning is increasingly used in a wide set of applications ranging from recommendation engines to autonomous systems through business intelligence and smart assistants. Designing and developing machine learning systems is a complex process that can be eased by leveraging effective design decisions tackling the most important challenges and by having a good system and software architecture. Goal: The research goal of this work is to identify common challenges, best design practices, and main software architecture design decisions of machine learning enabled systems from the point of view of researchers and practitioners. Method: We performed a mixed method including a systematic literature review and expert interviews. We started with a systematic literature review. From an initial set of 3038 studies, we selected 41 primary studies, which we analysed according to a data extraction, analysis, and synthesis process. In addition, we conducted 12 expert interviews that involved researchers and professionals with machine learning expertise from 9 different countries. Findings: We identify 35 design challenges, 42 best practices and 27 design decisions when architecting machine learning systems. By eliciting main design challenges, we contribute to best practices and design decisions. In addition, we identify correlations among design challenges, decisions and best practices. Conclusions: We believe that practitioners and researchers can benefit from this first and comprehensive analysis of current software architecture design challenges, best practices, and design decisions. 

In the system development of modern railroad vehicles, engineers frequently use a plethora of diverse notations to specify various systems, subsystems, and their associated concerns. The use of diverse notations introduces complex challenges linked with their management and integration. Conventional practices, which rely on manual revisions and translations, prove to be both time-intensive and cost-prohibitive. In addition, they carry substantial risks of human error, thereby potentially introducing faults into the system. Such practices are deemed inadequate for the railway industry, which is safety-critical in its nature and places paramount importance on the assurance of reliability and data integrity. To address these challenges, we developed a regular expression-based system facilitating the automatic translation of semi-structured texts into structured data, with a particular focus on ensuring data integrity and reliability. We have defined the system capitalizing on the insights and practical experience of our industrial partner, Alstom Rail Sweden AB, and validated it within their development process. The validation demonstrated the practicality of the system in a real-world context and highlighted valuable lessons learned throughout the process. Building on these insights, we applied model-driven engineering principles to generalize the system, providing an automated solution to the data extraction challenge from tender documents in the railway domain. 

Software architectures are pivotal in the success of software-intensive systems and serve as foundational elements that significantly impact the overall software quality. Reference architectures abstract software elements, define main responsibilities and interactions within a domain, and guide the architectural design of new systems. Using reference architectures offers advantages like enhanced interoperability, cost reduction through reusability, decreased project risks, improved communication, and adherence to best practices. However, these benefits are most pronounced when software architectures align with reference architectures. Deviations from prescribed reference architectures can nullify these benefits. Uncontrolled misalignment can become prohibitively expensive, necessitating costly redevelopments, with maintenance costs reaching up to 90% of development costs. Conformance-checking processes and identifying and resolving violations in the software architecture are essential to mitigate misalignment. To address these challenges, we introduce the concept of continuous conformance that is expressed as a distance function, together with a process supporting it. Continuous conformance quantifies the degree to which a software architecture adheres to a designated reference architecture. The conformance concept enables multi-level, incremental, and non-blocking checking and restoration tasks and allows the check of partial architectures without obstructing the design process. We operationalize this process through an assistive modeling tool to architect an Internet of things-based system. 

Over the last two decades, vehicles have undergone a significant shift, transforming into highly software-intensive systems. Projections indicate that even entry-level vehicles will soon integrate hundreds of millions of lines of code and incorporate numerous electronic control units. To navigate the complexity of these software-intensive systems, there has been a notable shift towards adopting model-driven engineering and specialised modelling languages. Among these languages, the Rubus Component Model has played a crucial role for over 25 years, supporting the development and timing analysis of distributed resource-constrained embedded systems. The enduring success of the Rubus Component Model lies in its responsiveness to end-users' demands and its ability to adapt to technological advancements. Notably, the proliferation of network interface controllers, including controller area network controllers, supporting diverse message-receiving policies like polling and interrupt, represents a significant advancement. However, the implications of these policies on end-to-end delays in distributed systems necessitate explicit modelling and dedicated timing analysis tools.This paper introduces an evolved Rubus Component Model, tailored for model-driven development and timing analysis in distributed embedded systems that utilise network interface controllers with diverse message-receiving policies. Drawing inspiration from a real-world example, the paper introduces new elements and properties in the Rubus Component Model designed to support these policies and facilitate timing analysis. The practical application of these enhancements is demonstrated, and insights are extended to other contemporary modelling languages in the vehicular domain. Beyond bolstering expressiveness, this evolution ensures the timing predictability of distributed embedded systems, aligning seamlessly with the Rubus Component Model's core focus.