This paper presents a novel approach for the semantic comparison of Asset Administration Shells (AAS), i.e., digital twins within the context of Industry 4.0. As digital twins gain importance in optimizing industrial processes, ensuring interoperability and accurate versioning of AAS becomes critical. We propose a method that focuses on object model-level comparisons rather than mere syntactic differences, enabling a more meaningful assessment of semantic equivalence. Our findings highlight the challenges associated with traditional comparison methods and suggest a comprehensive framework for enhancing AAS versioning and integrity verification.

Legacy assembly diagrams, often provided as machine-unreadable images, hinder integration into the Industry 4.0 (I4.0) ecosystem. We propose a multi-step pipeline leveraging Large Language Models as a single simple-to-operate tool to recognize parts, relationships, and global identifiers, segment parts in images, and structure the data into Asset Administration Shell as I4.0 digital twins. Targeted prompts are designed for each step and evaluated on 15 diverse real-world diagrams. Results show that while the LLM reliably recognizes parts and link identifiers, there are still some open challenges with relationship extraction and semantic segmentation. Despite these limitations, LLMs provide a viable tool for semi-automated digitalization. Our end-to-end pipeline thus enables seamless integration of legacy diagrams into I4.0 systems.

In the context of Industry 4.0, the automatic code and test generation from state diagrams embedded in specifications is a critical challenge for software correctness. In this paper we present an approach that leverages Large Language Models (LLMs) for the recognition of state diagrams to generate code and unit tests automatically. We compare the performance of LLMs with traditional computer vision models, highlighting the advantages of LLMs in terms of generalization and simplicity of setup. The results on two prominent industrial communication protocols, PROFINET and OPC UA, demonstrate the applicability of the approach, achieving significant reductions in manual effort and improving the accuracy of code and test generation.

In this work, we present Fences, a tool to generate sample data for arbitrary JSON Schemas. Fences generates these samples in three basic steps: First, the schema is simplified and normalized using boolean algebra. Second, the schema is mapped to a flow graph. And third, the flow graph is analyzed to obtain the final sets of JSON data. Besides valid samples, Fences also generates invalid samples it knows to be rejected by the schema. By combining these sets, all degrees of freedom offered by the schema are covered systematically. We assess the feasibility and correctness of our approach using synthetic schemas from the official JSON Schema test suite and a real-world use case from the Industry 4.0 domain. Additionally, we introduce schema coverage as metric to assess the completeness of our approach.

As Industrie 4.0 (I4.0) technologies continue to advance, ensuring the reliability and robustness of emerging standards like Asset Administration Shells (AAS) becomes paramount. This paper introduces a comprehensive testing approach for reactive (a.k.a. Type 2) AAS implementations, addressing the challenges posed by their intricate and dynamic nature. Our methodology adopts a design-by-contract approach to formalize API behavior. We design the client as a test oracle, where code contracts extensively validate the AAS operations, their interactions and effects. The related test cases cover various operation chains, including superpaths, service specifications, and serialization modifiers. The objective of this approach is to enhance the reliability and effectiveness of testing in the evolving landscape of AAS Type 2 implementations.

Digital twins, the virtual representations of physical assets, processes or systems, are becoming increasingly important in cyber-physical systems. They are a foundational block of Industry 4.0, the movement to digitalize industrial processes.The Asset Administration Shell (AAS) has been established as the preferable model to capture interoperable digital twins in the context of Industry 4.0. As asset administration shells become more prevalent, so does the need for specialized software development kits (SDKs) to manage them.The intertwined Industry 4.0 value chains are highly dependent on interoperability. The SDKs for AAS are often the direct interface between the components, and the malfunctioning of the SDKs leads to the breakage of the value chain. It is therefore important that we have the tools to determine how individual SDKs behave, and detect when they malfunction. In this work, we implement an approach to assessing the maturity of the SDKs for managing asset administration shells, and perform a thorough survey and evaluation of the existing SDKs.

Digital twins are a fundamental building block for the Industrie 4.0. With the Asset Administration Shell, a standard has been published that describes a meta model of such digital twins and the interfaces to access them. One of these interfaces is file-based access. Since the advent of the standard, various implementations have been developed. To ensure interoperability between different implementations, compliance with the specification must be verified. To test the correctness of a compliance check tool, a set of digital twins with known properties is used as test input. Since manually specifying such test models is error-prone, this work contributes an automated generation approach. It leverages flow graphs to represent the space of possible models. The subsequent path selection aims to generate expressive, yet efficient test input.

The digital twin, or more precisely the Asset Administration Shell, is the key element for interoperability in Industrie 4.0. Together with the asset, it forms the I4.0 component. For the development of I4.0 components, the availability of suitable software development tools is becoming increasingly relevant. However, it is not yet specified how these tools are to be systematically tested for correctness, compliance and conformity. These topics are the focus of this work. We show different established testing methods, evaluate their suitability and describe an approach for a capable test environment.