Bridging MDE and AI: A Systematic Review of Domain-Specific Languages and Model-Driven Practices in AI Software Systems Engineering
Simon Raedler, Luca Berardinelli, Karolin Winter, Abbas Rahimi, Stefanie Rinderle-Ma
TL;DR
The paper tackles the challenge of engineering AI-enabled software systems using model-driven engineering (MDE) approaches augmented with domain-specific languages (DSLs). Through a rigorous systematic literature review, it maps the current state of MDE4AI, analyzes the role of language workbenches, and assesses how well existing DSLs cover AI development phases per CRISP-DM. It finds that while language engineering for AI support is relatively mature, the field remains scattered and focused on individual phases (notably training) with limited data-prep coverage and scarce end-to-end tooling. The study highlights a need for consolidated methodologies, broader domain coverage, and closed-loop development to advance practical MDE4AI adoption in real-world AI software engineering. Overall, it provides a baseline of current practices and clearly outlines opportunities for tooling, standardization, and cross-domain collaboration to mature MDE4AI practices.
Abstract
Background:Technical systems are growing in complexity with more components and functions across various disciplines. Model-Driven Engineering (MDE) helps manage this complexity by using models as key artifacts. Domain-Specific Languages (DSL) supported by MDE facilitate modeling. As data generation in product development increases, there's a growing demand for AI algorithms, which can be challenging to implement. Integrating AI algorithms with DSL and MDE can streamline this process. Objective:This study aims to investigate the existing model-driven approaches relying on DSL in support of the engineering of AI software systems to sharpen future research further and define the current state of the art. Method: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 MDE principles and practices and the phases of AI development support aligned with the stages of the CRISP-DM methodology. Results:The study's findings show that language workbenches are of paramount importance in dealing with all aspects of modeling language development and are leveraged to define 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. Early project phases that support interdisciplinary communication of requirements, e.g., CRISP-DM Business Understanding phase, are rarely reflected. Conclusion: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.