Model-based Maintenance and Evolution with GenAI: A Look into the Future

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.