LLMs for Model-driven Engineering: A Systematic Literature Review

Model-Driven Engineering (MDE) supports the development of large-scale, complex systems through abstraction, automation, and traceability. The rise of Large Language Models (LLMs) has renewed interest in reshaping MDE, with recent studies exploring their use in tasks such as model generation, model comprehension, as well as model to code and test generation. While early results suggest improved productivity and lower barriers to modeling, their overall impact remains unclear. To address this, we conduct a Systematic Literature Review (SLR) and analyze 235 primary studies. We examine MDE tasks addressed, how LLMs were leveraged, benchmarking and evaluation practices, and the availability of tools and datasets. Results indicate a rapidly growing research trend, covering 25 types of MDE tasks, with model generation as the dominant focus. Unified Modeling Language (UML), Domain-Specific Languages (DSLs), and Business Process Model and Notation (BPMN) are the most frequently targeted formalisms. GPT-family models and zero-shot prompting are most commonly used, with fine-tuning and retrieval-augmented generation (RAG) as typical enhancement techniques. API-based LLM access and single-LLM architectures are the prevailing design choices. Benchmarking and evaluation studies remain narrow in scope. Evaluations primarily emphasize correctness, with limited benchmarking in realistic settings and insufficiently comprehensive replication materials.