Model-Based Engineering (MBE) has streamlined software development by focusing on abstraction and automation. The adoption of MBE in Maintenance and Evolution (MBM&E), however, is still limited due to poor tool support and a lack of perceived benefits. We argue that Generative Artificial Intelligence (GenAI) can be used as a means to address the limitations of MBM&E. In this sense, we argue that GenAI, driven by Foundation Models, offers promising potential for enhancing MBM&E tasks. With this possibility in mind, we introduce a research vision that contains a classification scheme for GenAI approaches in MBM&E considering two main aspects: (i) the level of augmentation provided by GenAI and (ii) the experience of the engineers involved. We propose that GenAI can be used in MBM&E for: reducing engineers' learning curve, maximizing efficiency with recommendations, or serving as a reasoning tool to understand domain problems. Furthermore, we outline challenges in this field as a research agenda to drive scientific and practical future solutions. With this proposed vision, we aim to bridge the gap between GenAI and MBM&E, presenting a structured and sophisticated way for advancing MBM&E practices.

翻译：模型驱动工程（MBE）通过聚焦抽象与自动化，显著提升了软件开发的效率。然而，由于工具支持不足及感知效益有限，MBE在维护与演化（MBM&E）中的应用仍不广泛。我们认为，生成式人工智能（GenAI）可作为解决MBM&E现有局限性的有效途径。在此背景下，我们提出，由基础模型驱动的GenAI为增强MBM&E任务提供了广阔前景。基于这一可能性，我们提出一项研究愿景，其中包含针对MBM&E中GenAI方法的分类框架，该框架主要考量两个维度：（i）GenAI提供的增强层级，以及（ii）相关工程师的经验水平。我们主张GenAI可在MBM&E中发挥以下作用：降低工程师的学习曲线、通过推荐机制最大化效率，或作为理解领域问题的推理工具。此外，我们梳理了该领域面临的挑战，并将其转化为研究议程，以推动未来科学与实践层面的解决方案。通过提出这一愿景，我们旨在弥合GenAI与MBM&E之间的鸿沟，为推进MBM&E实践提供系统化、精细化的路径。