Context: Machine Learning (ML) has become widely adopted as a component in
many modern software applications. Due to the large volumes of data available,
organizations want to increasingly leverage their data to extract meaningful
insights and enhance business profitability. ML components enable predictive
capabilities, anomaly detection, recommendation, accurate image and text
processing, and informed decision-making. However, developing systems with ML
components is not trivial; it requires time, effort, knowledge, and expertise
in ML, data processing, and software engineering. There have been several
studies on the use of model-driven engineering (MDE) techniques to address
these challenges when developing traditional software and cyber-physical
systems. Recently, there has been a growing interest in applying MDE for
systems with ML components. Objective: The goal of this study is to further
explore the promising intersection of MDE with ML (MDE4ML) through a systematic
literature review (SLR). Through this SLR, we wanted to analyze existing
studies, including their motivations, MDE solutions, evaluation techniques, key
benefits and limitations. Results: We analyzed selected studies with respect to
several areas of interest and identified the following: 1) the key motivations
behind using MDE4ML; 2) a variety of MDE solutions applied, such as modeling
languages, model transformations, tool support, targeted ML aspects,
contributions and more; 3) the evaluation techniques and metrics used; and 4)
the limitations and directions for future work. We also discuss the gaps in
existing literature and provide recommendations for future research.
Conclusion: This SLR highlights current trends, gaps and future research
directions in the field of MDE4ML, benefiting both researchers and
practitioners

这项系统文献综述研究了 MDE 和机器学习（MDE4ML）交叉领域，分析了现有的研究动机、MDE 解决方案、评估技术、关键优势和局限性，并提供了未来研究的建议。

基于模型驱动的机器学习组件工程：一项系统性文献综述

Model-driven Engineering for Machine Learning Components: A Systematic  Literature Review

Objective: This study aims to investigate the existing body of knowledge in
the field of Model-Driven Engineering MDE in support of AI (MDE4AI) 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 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.
Results: 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 \textit{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.
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.

研究发现 MDE 对 AI 的使用仍处于早期阶段，并且没有广泛应用的单一工具或方法，当前的方法主要注重具体的开发阶段而非整个开发过程，因此，该研究提出了几个研究方向，以进一步改进 MDE 在 AI 方面的应用，并引导未来的研究。

面向人工智能的模型驱动工程：一项系统文献综述

Model-Driven Engineering for Artificial Intelligence -- A Systematic  Literature Review

Recently, several studies have proposed frameworks for Quantum Federated
Learning (QFL). For instance, the Google TensorFlow Quantum (TFQ) and
TensorFlow Federated (TFF) libraries have been deployed for realizing QFL.
However, developers, in the main, are not as yet familiar with Quantum
Computing (QC) libraries and frameworks. A Domain-Specific Modeling Language
(DSML) that provides an abstraction layer over the underlying QC and Federated
Learning (FL) libraries would be beneficial. This could enable practitioners to
carry out software development and data science tasks efficiently while
deploying the state of the art in Quantum Machine Learning (QML). In this
position paper, we propose extending existing domain-specific Model-Driven
Engineering (MDE) tools for Machine Learning (ML) enabled systems, such as
MontiAnna, ML-Quadrat, and GreyCat, to support QFL.

该研究提出了一种在机器学习系统中扩展现有领域特定的建模驱动工具来支持量子联邦学习的方法，该方法可以为开发人员提供一个从底层到联邦学习库的抽象层，以实现量子机器学习的最新技术。