Digital twins are sophisticated software systems for the representation, monitoring, and control of cyber-physical systems, including automotive, avionics, smart manufacturing, and many more. Existing definitions and reference models of digital twins are overly abstract, impeding their comprehensive understanding and implementation guidance. Consequently, a significant gap emerges between abstract concepts and their industrial implementations. We analyze popular reference models for digital twins and combine these into a significantly detailed unifying reference model for digital twins that reduces the concept-implementation gap to facilitate their engineering in industrial practice. This enhances the understanding of the concepts of digital twins and their relationships and guides developers to implement digital twins effectively.

Several software architectures for digital twins have been proposed, specifying their structure and key components, such as the ISO 23247 standard for manufacturing. However, systematic methodologies for developing digital twins in the manufacturing domain remain lacking. Existing research does not adequately address methodological aspects, such as composing essential digital twin components, defining their interfaces, and systematically analyzing relevant data. This gap presents a challenge for the structured development of digital twins. In this work, we introduce a systematic approach to identify key requirements for manufacturing digital twins and propose a model-driven method for their creation. Our approach enables the generation of executable digital twins based on a formalized specification. We demonstrate its applicability through multiple industrial manufacturing demonstrators, highlighting its suitability in practice. The proposed method is adaptable regarding the purpose of the digital twins to be developed and supports a largely automated process.

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.

Abstract Digital twins (DTs) are proliferating in a multitude of domains, including agriculture, automotive, avionics, logistics, manufacturing, medicine, smart homes, etc. As domain experts and software experts both have to contribute to the engineering of effective DTs, several model-driven engineering (MDE) approaches have been recently proposed to ease the design, development, and operation of DTs. However, the diversity of domains in which MDE is currently applied to DTs, as well as the diverse landscape of DTs and MDE applications to DTs, makes it challenging for researchers and practitioners to get an overview of what techniques and artifacts are already applied in this context. In this paper, we shed light on the aforementioned aspects by performing a systematic mapping study on the application of MDE automation techniques, i.e., model-to-model transformation, code generation, and model interpretation, in the context of DTs as well as on the characteristics of DTs including the twinned systems to which these techniques are applied in different domains. We systematically retrieved a set of 189 unique publications, of which 66 were selected for further investigation in this paper. Our results indicate that the distribution of employed MDE techniques (136 applications of automation techniques) is balanced between the different techniques, but there are significant variations for different DT types. With respect to the different domains, we found that even though applications are available in many domains, a small number of domains currently dominate applications of MDE to DTs, i.e., more than half of included papers are in the manufacturing and transportation domains.

Control software for production systems is typically developed by domain experts, despite its high complexity. The increasingly available Large Language Models (LLMs) can assist developers with code generation and debugging. However, their suitability for generating control software for production systems is still unexplored. Therefore, this study explores the generation of Structured Text (ST) according to IEC-61131-3 by different LLMs. We selected 21 coding examples that are representative of PLC programming and developed an approach for comparing the outputs of different LLMs using metrics for testing generated code (CodeBERTScore, pass@k, generation time). The strategies for prompt optimization that were developed as part of this work can be directly used for improved ST generation. Our results show that, at the time of the study, ChatGPT-4 had the highest reliability in generating syntactically correct ST code that expresses the desired functionality.

The demand for digital twins and suitable modeling techniques in the automotive industry is increasing rapidly. Yet, there is no common understanding of digital twins in automotive, nor are there modeling techniques established to create automotive digital twins. Recent studies on digital twins focus on the analysis of the literature on digital twins for automotive or in general and, thus, neglect the industrial perspective of automotive practitioners. To mitigate this gap between scientific literature and the industrial perspective, we conducted a questionnaire survey among experts in the German automotive industry to identify i) the desired purposes for and capabilities of digital twins and ii) the modeling techniques related to engineering and operating digital twins across the phases of automotive development. To this end, we contacted 189 members of the Software-Defined Car research project and received 96 responses. The results show that digital twins are considered most useful in the usage and support phase of automotive development, representing vehicles as-operated. Moreover, simulation models, source code, and business process models are currently considered the most important models to be integrated into a digital twin alongside the associated, established tools.

Engineering Cyber-Physical Production Systems (CPPSs) heavily relies on Domain-Specific Languages (DSLs), which are tailored to a specific class of problems inherent to CPPSs. DSLs enable non-programming experts to solve problems in their domain, such as modeling production processes, implementing control software, or managing the variability of production systems. A DSL ecosystem encompasses the entire infrastructure (e.g., libraries and tools) built around its language and contributes to the successful and easy adoption thereof by domain experts. We present a maturity evaluation model for DSL ecosystems serving two aims: to developers, it reveals missing but essential aspects of the ecosystem, and users (e.g., industrial companies) can evaluate the maturity of a DSL ecosystem. We propose criteria to evaluate the maturity of DSL ecosystems and apply them to existing, publicly available DSLs which have been adopted in CPPSs. The results demonstrate that all components of the model are covered and they allow for deriving hypotheses about DSL ecosystems used in CPPSs.

Low-code platforms have gained popularity for accelerating complex software engineering tasks through visual interfaces and pre-built components. Software language engineering, specifically language composition, is such a complex task requiring expertise in composition mechanisms and language workbenches multi-dimensional language constituents (syntax and semantics). This paper presents an extensible low-code platform with a graphical web-based interface for language composition. It enables composition using language components, facilitating systematic composition within language families promoting reuse and streamlining the management, composition, and derivation of domain-specific languages.

Cyber-Physical Production Systems (CPPSs) are envisioned as next-generation adaptive production systems combining modern production techniques with the latest information technology. A CPPS creates a complex environment between different domains (mechanical, electrical, software engineering), requiring multidisciplinary solutions to tackle growing complexity issues and reduce (maintenance) effort. Software plays an increasingly important role in assuring an effective and efficient operation of CPPSs. However, software engineering methods applied for CPPSs seem to lag behind modern software engineering methods, where tremendous progress has been made in the last years. We initiated the Software Engineering in Cyber-Physical Production Systems Workshop (SECPPS-WS) to analyze and overcome this gap. After two instances with mostly academic participants, we conducted a full-day workshop with nine industry representatives from eight companies that develop and maintain CPPSs. Each industry representative presented their current work and challenges. We collected these challenges and condensed a categorized list of challenges backed by industry statements and literature. This paper presents the resulting list and pointers to (partial) solutions to offer guidance for academia and identify promising research opportunities in this area.

Many engineering domains started using generic modeling languages, such as SysML, to describe or prescribe the systems under development. This raises a gap between the generic modeling languages and the domains of experience of the engineers using these. Engineering truly domain-specific languages (DSLs) for experts of these domains still is too challenging for their wide-spread adoption. One major obstacle, the inability to reuse multi-dimensional (encapsulating constituents of syntax and semantics) language components in a black-box fashion, prevents the effective engineering of novel DSLs. To facilitate engineering DSLs, we devised a concept of 3D components for textual, external, and translational DSLs that relies on systematic reuse through systematic closed and open variability in which DSL syntaxes can be embedded, well-formedness rules joined, and code generators integrated in a black-box fashion. We present this concept, a method for its systematic application, an integrated collection of modeling languages supporting systematic language reuse, and an extensible framework that leverages these languages to derive novel DSLs from language product lines. These can greatly mitigate many of the challenges in DSL reuse and, hence, can advance the engineering of truly domain-specific modeling languages.