Oct 2026· Proceedings of the ACM/IEEE 29th International Conference on Model Driven Engineering Languages and Systems· 1 citation· 53 references
TL;DR
A Systematic Literature Review of 96 peer-reviewed studies on the use of LLMs in MDE is presented, aiming to consolidate existing knowledge, highlight emerging patterns, and identify key limitations and research opportunities for future advancement.
Abstract
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 design, model management, 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 core activities are currently assisted and the types of modeling languages and artifacts involved. We further examine how LLMs are used to support these activities and the role of human involvement in the loop. Finally, we address the maturity of these solutions in terms of technology readiness level, domain applicability, and the availability of supporting tools.
Business Process Modeling (BPM) is a key and complex activity in Software and Systems Engineering. As a consequence, any relevant assistance integrated into the BPM tooling can be highly beneficial. Historically, Natural Language Processing (NLP) techniques have already been used in this context. However, the recent de...
Andrey Sadovykh, Loay Chlih, Bilal Said et al.· Proceedings of the ACM/IEEE...· 0 citations
Welcome to the 16th International Workshop on Model-Driven Requirements Engineering (MoDRE’26), held in conjunction with the 34th edition of the Requirements Engineering (RE) Conference. The MoDRE workshop series has established a forum where researchers and practitioners can discuss the challenges and opportunities of...
A. Moreira, Gunter Mussbacher, João Araújo et al.· 2026 IEEE 34th International...· 0 citations
Large Language Models (LLMs) can assist developers and modelers in many software engineering activities. In the context of model-driven engineering, diagnosing and fixing errors in software specifications is a challenging task, even for human experts. Errors can be subtle, involving a combination of constraints and mod...
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This research affirms the necessity of a human-in-the-loop paradigm in the EA discipline within this specific context and suggests that LLMs function effectively as augmentation accelerators rather than human replacements, thereby shifting the enterprise architect's focus from manual drafting to analytical validation a...
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By moving from isolated instruction crafting to system-level contextual orchestration, context engineering reframes how large language model (LLM) applications are designed for complex, multi-source workflows. Prompt engineering can deliver strong results for narrow, single-turn tasks, yet it often becomes brittle and...
Attila Kovari· Discover Artificial Intellig...· 0 citations
In an era where software development is deeply tied with Large Language Models, does Model Driven Engineering (MDE) still make sense? This raises the question of the extent to which MDE can be successfully combined with an LLM approach to address the downsides of each approach separately. In this paper, we try to answe...
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