M. Eisenberg, S. Klikovits, M. Wimmer, K. Wieland: Towards LLM-enhanced Conflict Detection and Resolution in Model Versioning, NIER Track@MODELS 2025, ACM / IEEE 28th International Conference on Model Driven Engineering Languages and Systems, October 8, 2025, Grand Valley State University, Grand Rapids, MI, USA and Virtual. Doi: 10.1109/MODELS67397.2025.00032
In this paper, we explore how Large Language Models (LLMs) can augment model versioning workflows by supporting conflict detection and resolution. In particular, we present an LLM-enhanced solution for detecting conflicts in the three-way model merging setting. Drawing on a collection of conflict types from prior literature, we demonstrate how an LLM assistant can 1) pinpoint conflicting changes and 2) provide resolution options with clear rationales and explanations of their implications. Our results indicate that the LLMs’ access to a broad range of domains and modeling languages can help find and resolve complex versioning conflicts. Our implementation combines the industrial tool LemonTree for analyzing models and model changes, with a GPT-4o (LLM) assistant primed with relevant context to detect and resolve conflicts. We conclude by discussing directions for future research to improve model versioning workflows using LLMs.