Large Language Models as an Assistant to Interpret UML Models in Model-Based Engineering: An Exploratory Study
Hassan Bashiri, Alireza Khalilipour, Parsa Bakhtiari, Moharram Challenger · Sustainable Energy and Artificial Intelligence · 2025
Creating a formal common language, beyond the ambiguities of natural languages, between different stakeholders, from analysts to test engineers, is one of the key goals of software modeling. Although notations are standard in software modeling languages such as UML, junior engineers’ interpretation of models varies. Model interpretation in the presence of experienced people increases the learning rate for junior engineers. One of the potentials of large language models is the ability to interpret images and models. This research aims to use large language models as an assistant to interpret UML models to increase junior engineers’ learning rate and understanding of the software models. We conducted an evaluation study to examine how helpful an LLM can be to help interpret the software models. Although large language models are still not very accurate in interpreting UML models, the experiment’s results showed that students’ learning rates increased by LLMs as model interpretation assistants. In other words, the large language model worked well as a teaching assistant. The detailed results of this exploratory study are reported in this paper.