Technical systems are becoming increasingly complex due to the increasing number of components, functions, and involvement of different disciplines. In this regard, model-driven engineering techniques and practices tame complexity during the development process by using models as primary artifacts. Modeling can be carried out through domain-specific languages whose implementation is supported by model-driven techniques. Today, the amount of data generated during product development is rapidly growing, leading to an increased need to leverage artificial intelligence algorithms. However, using these algorithms in practice can be difficult and time-consuming. Therefore, leveraging domain-specific languages and model-driven techniques for formulating AI algorithms or parts of them can reduce these complexities and be advantageous. This study aims to investigate the existing model-driven approaches relying on domain-specific languages in support of the engineering of AI software systems to sharpen future research further and define the current state of the art. We conducted a Systemic Literature Review (SLR), collecting papers from five major databases resulting in 1335 candidate studies, eventually retaining 18 primary studies. Each primary study will be evaluated and discussed with respect to the adoption of (1) MDE principles and practices and (2) the phases of AI development support aligned with the stages of the CRISP-DM methodology. The study’s findings show that language workbenches are of paramount importance in dealing with all aspects of modeling language development (metamodel, concrete syntax, and model transformation) and are leveraged to define domain-specific languages (DSL) explicitly addressing AI concerns. The most prominent AI-related concerns are training and modeling of the AI algorithm, while minor emphasis is given to the time-consuming preparation of the data sets. Early project phases that support interdisciplinary communication of requirements, such as the CRISP-DM Business Understanding phase, are rarely reflected. The study found that the use of MDE for AI is still in its early stages, and there is no single tool or method that is widely used. Additionally, current approaches tend to focus on specific stages of development rather than providing support for the entire development process. As a result, the study suggests several research directions to further improve the use of MDE for AI and to guide future research in this area.

CAEX is one of the most promising standards when it comes to data exchange between engineering tools in the production system automation domain. This is also reflected by the current emergence of AutomationML, which uses CAEX as its core representation language. However, with the increasing use of CAEX, important language engineering challenges arise. One of these challenges is the customization of CAEX for its usage in superior standards, such as AutomationML, which requires the precise specification of the language including the formalization and validation of additional usage rules. Another highly topical challenge is the ongoing evolution of CAEX as has recently happened with the transition from version 2.15 to version 3.0. Further challenges include the provisioning of editing facilities and visualizations of CAEX documents such that they can be inspected and modified by engineers, and the development of transformations from and to CAEX such that different engineering artifacts can be exchanged via CAEX.

This paper presents a systematic mapping study classifying existing scientific contributions on synergies of Model Driven Engineering (MDE), Artificial Intelligence/Machine Learning (AI/ML), and DevOps, with the overall objective of supporting the continuous development of Cyber-Physical Systems (CPSs). We collected papers from bibliographic sources and selected primary studies to analyse. Then, we characterised and classified the current state of the art, focusing on 1) main aspects already tackled at the intersection of at least two of the three studied areas, and 2) findings emerging from the analysis as a framework for potential future research, notably regarding the integration of the three studied areas. The results reveal that few approaches combine MDE, AI/ML, and DevOps for software and systems engineering. In contrast, several approaches have combined two of them, specifically MDE and DevOps. Approaches combining AI/ML with MDE or DevOps are also becoming more frequent and will most likely continue to progress in the future. These synergies cover a range of engineering activities, from requirements and design to monitoring, maintenance, and evolution. Open research challenges include advancing AI/ML, MDE, and DevOps integration, supporting scalable, data-oriented solutions, proposing new continuous engineering methods, and adapting DevOps practices to diverse systems.