Bridging MDE and AI: A Systematic Review of Domain-Specific Languages and Model-Driven Practices in AI Software Systems Engineering
This repository contains the artifacts of the Systematic Literature Review of Domain-Specific Languages and Model-Driven Practices in AI Software Systems Engineering.
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 in a joined project with WIN-SE – Institut für Wirtschaftsinformatik – Software Engineering (jku.at)
Technical systems are becoming increasingly complex due
to the increasing number of components, functions, and involvement of dif-
ferent 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 al-
gorithms. 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 ap-
proaches 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, even-
tually retaining 18 primary studies. Each primary study will be evaluated
and discussed with respect to the adoption of (1) MDE principles and prac-
tices 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 commu-
nication 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.
Raedler, S., Berardinelli, L., Winter, K., Rahimi, A., & Rinderle-Ma, S. (2023). Bridging MDE and AI: A Systematic Review of Domain-Specific Languages and Model-Driven Practices in AI Software Systems Engineering (Version 2). Version 2. arXiv. https://doi.org/10.48550/ARXIV.2307.04599