This repository contains the artifacts of the Systematic Literature review on Model-driven engineering for artificial intelligence.
This work was supervised and elaborated at the Chair of Information Systems and Business Process Management (i17), Department of Computer Science, Technical University of Munich
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 (MDE) techniques and practices tame complexity during the development process by using models as primary artifacts. Today, the amount of data generated during product development is rapidly growing, leading to an increased need for leveraging Artificial Intelligence (AI) algorithms. However, using these algorithms in practice can be difficult and time-consuming. Therefore, utilizing MDE techniques and tools for formulating AI algorithms or parts of them can reduce these complexities and be advantageous.
This study aims to investigate the existing body of knowledge in the field of MDE in support of AI (MDE4AI) 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 703 candidate studies, eventually retaining 15 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 the pillar concepts of MDE (metamodel, concrete syntax and model transformation), are leveraged to define domain-specific languages (DSL) explicitly addressing AI concerns. Different MDE technologies are used, leveraging different language workbenches. 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.
Simon Rädler, Luca Berardinelli, Karolin Winter, Abbas Rahimi, and Stefanie Rinderle-Ma. Model-Driven Engineering for Artificial Intelligence – A Systematic Literature Review, July 2023. doi:10.48550/arXiv.2307.04599.