Model-based Maintenance and Evolution with GenAI: A Look into the Future
Luciano Marchezan, Wesley K. G. Assunção, Edvin Herac, Alexander Egyed
TL;DR
This paper addresses the low industrial adoption of model-based maintenance and evolution (MBM&E) by arguing that Generative AI, guided by Foundation Models, can address key limitations. It introduces a two-axis classification framework that pairs augmentation level with engineer experience to define four MBM&E research directions: Assisting, Leveling Up, Reasoning, and Automating, alongside the Software 4.0 Agentware concept. A concrete research agenda follows, detailing data-set challenges, accuracy vs. usefulness trade-offs, domain-specific prompting, and practical industry contributions to bridge GenAI and MBM&E. The work aims to steer future MBM&E research and practice toward structured, incremental integration of GenAI techniques that support engineers throughout maintenance and evolution activities.
Abstract
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.