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.

With the increase of technologies such as the Internet of Things (IoT) and Cyber-Physical Systems, a huge amount of data is generated by current systems. To gain insights from this data, it must be combined with meta-information about its origins. Therefore, Digital Twins (DTs), as a common representation of a system and its data, are currently gaining traction in both industry and academia. However, these DTs have of course to be evolvable in order to reflect the high need of flexibility of the systems to support extensions, adaptations, customizations, etc. Evolving the DT representations currently not only involves a lot of manual effort, but might also lead to loss of data if not done correctly. To provide dedicated evolution support, we propose a dedicated framework for realizing evolution strategies between the schema, instance, and data level of a DT. In particular, we present a fluent API which allows the flexible but systematic manipulation of DTs during runtime and demonstrate its usage for a use case.

As technologies such as the Internet of Things (IoT) and Cyber-Physical Systems (CPS) are becoming ubiquitous, systems adopting these technologies are getting increasingly complex. Digital Twins (DTs) provide comprehensive views on such systems, the data they generate during runtime, as well as their usage and evolution over time. Setting up the required infrastructure to run a Digital Twin is still an ambitious task that involves significant upfront efforts from domain experts, although existing knowledge about the systems, such as engineering models, may be already available for reuse. To address this issue, we present AML4DT, a model-driven framework supporting the development and maintenance of Digital Twin infrastructures by employing AutomationML (AML) models. We automatically establish a connection between systems and their DTs based on dedicated DT models. These DT models are automatically derived from existing AutomationML models, which are produced in the engineering phases of a system. Additionally, to alleviate the maintenance of the DTs, AML4DT facilitates the synchronization of the AutomationML models with the DT infrastructure for several evolution cases. A case study shows the benefits of developing and maintaining DTs based on AutomationML models using the proposed AML4DT framework. For this particular study, the effort of performing the required tasks could be reduced by about 50%.

In the area of Cyber-Physical Systems (CPS), the degree of complexity continuously increases mainly due to new key-enabling technologies supporting those systems. One way to deal with this increasing complexity is to create a digital representation of such systems, a so-called Digital Twin (DT), which virtually acts in parallel ideally across the entire life-cycle of a CPS. For this purpose, the DT uses simulated or real-time data to mimic operations, control, and may modify the CPS's behaviour at runtime. However, building such DTs from scratch is not trivial, mainly due to the integration needed to deal with heterogeneous systems residing in different technological spaces. In order to tackle this challenge, Model-Driven Engineering (MDE) allows to logically model a CPS with its physical components. Usually, in MDE such "logical models" are created at design time which keep them detached from the deployed system during runtime. Instead of building bilateral solutions between each runtime environment and every engineering tool, a dedicated integration layer is needed which can deal with both, design and runtime aspects. Therefore, we present a reference architecture that allows on the one side to query data from model repositories to enrich the running system with design-time knowledge, and on the other side, to be able to reasoning about system states at runtime in design-time models. We introduce a model repository query and management engine as mediator and show its feasibility by a demonstration case.