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

TL;DR

This paper explores how Generative AI, driven by Foundation Models, can enhance Model-Based Maintenance and Evolution by improving efficiency, reducing learning curves, and serving as a reasoning tool, thus advancing the field.

Contribution

It introduces a research vision and classification scheme for applying GenAI in MBM&E, addressing current limitations and outlining future challenges.

Findings

Proposes a classification scheme for GenAI in MBM&E

Identifies potential benefits like reduced learning curve and improved reasoning

Outlines a research agenda with challenges and future directions

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.