Large Language Models Assistance in core Model-Driven Engineering activities

Recent research explores the use of Large Language Models (LLMs) and conversational agents to enable automation and assist modeling tasks across the Model-Driven Engineering (MDE) lifecycle, including model generation, refinement, transformation, and evolution.This paper presents a Systematic Literature Review of 96 peer-reviewed studies on the use of LLMs in MDE, aiming to consolidate existing knowledge, highlight emerging patterns, and identify key limitations and research opportunities for future advancement. We systematically analyze the literature to identify which model management tasks are currently supported, and the type of modelling languages and artifacts involved. We further examine how LLMs are used to perform these tasks and what the role of human involvement is in the loop. Finally, we address the maturity level of these solutions in terms of technology readiness level, domain applicability, and the availability of supporting tools.