Understanding and maintaining software systems often requires extracting high-level abstractions, such as domain models, from source code. textit{MDRE-LLM} addresses this challenge by integrating Large Language Models (LLMs) with traditional Model-Driven Reverse Engineering (MDRE) techniques, offering an innovative approach to automate and enhance domain model recovery. The tool supports flexible granularity strategies, enabling precise control over recovery detail, and includes model comparisons to validate the accuracy of LLM-generated models against baseline models retrieved deterministically.

MDRE-LLM addresses diverse use cases, including analyzing legacy systems with minimal documentation, rapidly comprehending large-scale codebases, validating LLM performance in reverse engineering tasks, and generating reproducible datasets that pair Java projects with their domain models. These capabilities have the potential to improve software analysis and refactoring while advance AI-driven research and education by fostering systematic experimentation and collaboration.