Digital twins (DTs) encapsulate the concept of a real-world entity (RE) and corresponding bidirectionally connected virtual one (VE) mimicking certain aspects of the former in order to facilitate various use-cases such as predictive maintenance. DTs typically encompass various models that are often developed by experts from different domains using diverse tools. To maintain consistency among these models and ensure the continued functioning of the system, effective identification of any consistency issues and addressing them whenever necessary is imperative. In this paper, we investigate the concept of consistency management and propose a consistency management framework that addresses various characteristics of DT models. Subsequently, we present three working examples that implement the proposed framework with graph-based techniques. Taking the working examples into account, we demonstrate and argue that our consistency management framework can provide crucial assistance in the consistency management of DT models.

Domain-Specific languages (DSLs) bridge the gap between the domain-specific problem space and the solution space of software engineering. Engineering DSLs is a complex and time-intensive iterative process involving exchanges with stakeholders who amongst others decide on the DSL’s syntax. Since in this process the stakeholder requirements change frequently, so can the corresponding DSL. The subsequent changes to the language specification may produce conflicts that language engineers need to be aware of and resolve. Current research has not adequately answered the question which change operations for grammar-based syntax exist, and which impact they have at meta-model and model level. To answer this question we develop a taxonomy of change types for grammars of textual DSLs that includes the concepts typically found in grammar-based language workbenches such as Xtext, MontiCore, and Neverlang, and lists the possible change operations that can be performed. The taxonomy was built iteratively based on an Xtext based implementation of the Systems Modeling Language v2 and evaluated in a case study that leverages the taxonomy to perform impact analysis. The taxonomy presented in this paper will help language engineers to analyse the impact of changes to the grammar-based syntax specification of a language and to utilize this analysis, e.g., to perform historical change impact analysis.

Digital twins (DTs) encapsulate the concept of a real-world entity (RE) and corresponding bidirectionally connected virtual one (VE) mimicking certain aspects of the former in order to facilitate various use-cases such as predictive maintenance. DTs typically encompass various models that are often developed by experts from different domains using diverse tools. To maintain consistency among these models and ensure the continued functioning of the system, effective identification of any consistency issues and addressing them whenever necessary is imperative. In this paper, we investigate the concept of consistency management and propose a consistency management framework that addresses various characteristics of DT models. Subsequently, we present two case-studies that implement the proposed framework with graph-based techniques. Taking into account both case-studies, we argue that the graph-based approaches have significant potential and invite further exploration.

Abstract Digital twins (DTs) are often defined as a pairing of a physical entity and a corresponding virtual entity (VE), mimicking certain aspects of the former depending on the use-case. In recent years, this concept has facilitated numerous use-cases ranging from design to validation and predictive maintenance of large and small high-tech systems. Various heterogeneous cross-domain models are essential for such systems, and model-driven engineering plays a pivotal role in the design, development, and maintenance of these models. We believe models and model-driven engineering play a similarly crucial role in the context of a VE of a DT. Due to the rapidly growing popularity of DTs and their use in diverse domains and use-cases, the methodologies, tools, and practices for designing, developing, and maintaining the corresponding VEs differ vastly. To better understand these differences and similarities, we performed a semi-structured interview research with 19 professionals from industry and academia who are closely associated with different lifecycle stages of digital twins. In this paper, we present our analysis and findings from this study, which is based on seven research questions. In general, we identified an overall lack of uniformity in terms of the understanding of digital twins and used tools, techniques, and methodologies for the development and maintenance of the corresponding VEs. Furthermore, considering that digital twins are software intensive systems, we recognize a significant growth potential for adopting more software engineering practices, processes, and expertise in various stages of a digital twin’s lifecycle.

In recent years, machine learning (ML) based digital twins (DTs) have seen widespread application in the anomaly detection domain. A search-based literature survey revealed that the majority of the case studies focus on large-scale systems (i.e., nuclear power plant, aerospace, and power grid) producing extensive data. Our work aims to investigate the performance of this technology in smaller-scale systems that generate less data. In this case study, we developed a ML-based DT of the mobility system, the omni wheels, of the Turtle soccer robots. The DT is capable of analyzing historical data collected from the physical robots and differentiating between damaged and undamaged wheels. Our experiments suggest that ML-based DT of small-scale systems is indeed capable of achieving relatively accurate results for anomaly detection use-cases.