Model-driven engineering (MDE) is a software engineering paradigm based on the systematic use of models. Over the past few years, engineers have significantly increased the use of MDE, which has been reported as a successful paradigm for developing industrial software. Recently, there have also been remarkable advancements in the Artificial Intelligence (AI) domain, with a significant increase in advanced Machine Learning (ML) techniques. The advances in both fields have led to a surge in works that dwell within the intersection of ML and MDE. This work places the focus on systematically reviewing works that leverage ML to solve MDE problems. We have reviewed a total of 9,194 papers, selecting 98 studies for further analysis. The results of our Systematic Literature Review (SLR) bring light to the current state of the art and trends in the field, discussing the drift in the usage of the different available ML techniques along with the remaining research gaps and open challenges. Our SLR has the potential to produce a positive impact in the research community by steering it towards ML techniques that have been successfully applied to solve MDE challenges.

中文翻译：

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使用机器学习的模型驱动工程的系统文献综述


模型驱动工程（MDE）是一种基于模型的系统使用的软件工程范例。在过去几年中，工程师显着增加了 MDE 的使用，据报道，MDE 是开发工业软件的成功范例。最近，人工智能（AI）领域也取得了显着的进步，先进的机器学习（ML）技术显着增加。这两个领域的进步导致了 ML 和 MDE 交叉领域的工作激增。这项工作的重点是系统地回顾利用 ML 解决 MDE 问题的工作。我们总共审阅了 9,194 篇论文，选择了 98 项研究进行进一步分析。我们的系统文献综述 (SLR) 的结果揭示了该领域的当前技术水平和趋势，讨论了不同可用 ML 技术的使用趋势以及剩余的研究差距和开放挑战。我们的 SLR 有可能通过引导研究社区采用已成功应用于解决 MDE 挑战的 ML 技术来对研究社区产生积极影响。