Toward a Community-Curated Golden Dataset of UML Models

Abstract

Datasets of Unified Modeling Language (UML) models are becoming increasingly valuable for education, empirical research, and tool development in model-driven engineering (MDE) and conceptual modeling. In recent years, several datasets have emerged - mostly compiled through automated crawling of open platforms such as GitHub and GenMyModel. While these efforts have improved access to real-world modeling artifacts, the resulting collections often suffer from serious quality issues: they include syntactically invalid models, semantically incorrect structures, and placeholder or dummy content. Moreover, most models are not accompanied by textual domain descriptions, which are essential for understanding the intent behind the model and assessing its semantic soundness. Therefore these model datasets are far from ideal as a source for modeling exercises or empirical MDE research. This paper presents an initial step toward a community-curated golden dataset of UML models, designed to address these limitations. Our contribution includes i) a curated set of UML models, each paired with a natural language description of the modeled domain requirements, ii) a publicly accessible web platform for exploring and querying the dataset, and iii) a structured process for community-based contribution and evaluation to support sustainable growth and quality assurance of the dataset. By fostering community involvement and providing high-quality, semantically grounded models, this work lays the foundation for a widely accepted benchmark dataset in UML-based research and education.