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.

翻译：背景：技术系统正随着跨学科组件与功能的增加而日趋复杂。模型驱动工程（MDE）通过将模型作为核心制品来管理这种复杂性，而领域特定语言（DSL）在MDE的支持下为建模提供便利。随着产品开发中数据生成量的增加，对AI算法的需求日益增长，但实现这些算法可能具有挑战性。将AI算法与DSL及MDE集成可简化这一过程。目的：本研究旨在调查现有基于MDE和DSL的AI软件系统工程方法，以进一步明确未来研究方向并确立当前技术发展水平。方法：我们采用系统性文献综述（SLR）方法，从五大数据库中收集论文，共获得1335篇候选研究，最终保留18篇主要研究。每项主要研究将依据MDE原则与实践的采纳程度，以及与CRISP-DM方法论各阶段对应的AI开发支持阶段进行评价与讨论。结果：研究发现，语言工作台在应对建模语言开发的各个方面具有至关重要的作用，并被用于显式定义面向AI问题的DSL。最受关注的AI相关问题是AI算法的训练与建模，而对耗时的数据准备阶段关注较少。早期项目阶段（如CRISP-DM业务理解阶段）中支持需求跨学科沟通的内容鲜有体现。结论：研究表明，MDE在AI领域的应用仍处于早期阶段，尚无广泛使用的单一工具或方法。此外，当前方法往往聚焦于开发过程中的特定阶段，而非提供贯穿整个开发流程的支持。