发表机构
Institute for Software and Systems Engineering, TU Clausthal（克劳斯塔尔工业大学软件与系统工程研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对自动化领域建模缺乏标准化基准的问题，引入整合两类数据集的基准，评估多种建模方法，为该领域研究提供可复用的研究成果。
AI 中文摘要
领域建模在领域驱动设计中发挥着关键作用，用于捕获特定领域内的核心实体及其关系。尽管自动化领域建模已取得进展，但缺乏标准化基准阻碍了现有方法的对比评估。本文引入一个基准以填补该空白：该基准整合了Zenodo上由Calamo、Mecella和Snoeck的Text2UML项目发布的含45条记录的Golden UML Modelset（Verbruggen等人，2025），以及Chen等人（2023a,b）的含8条记录的参考档案，可在不同复杂度和规模下评估自动化领域建模方法。给定自然语言描述，任务是生成对应领域模型，每条描述均提供参考领域模型作为真值，使用指标对比生成模型与真值模型。为展示基准的实用性，本文评估了多种自动化领域建模方法，包括基于启发式规则的方法和大语言模型（LLM）驱动的策略。该基准符合FAIR4RS建议（Chue Hong等人，2022），作为研究成果提供以鼓励复用，支持自动化领域建模的未来研究。
英文摘要
Domain modeling plays an essential role in domain-driven design, capturing essential entities and their relationships within a specific domain. Despite advancements in automated domain modeling, the absence of standardized benchmarks has hindered the comparative assessment of existing approaches. This paper introduces a benchmark designed to address this gap. The benchmark combines the 45-record Golden UML Modelset (Verbruggen et al., 2025) on Zenodo, as distributed by the Text2UML project of Calamo, Mecella, and Snoeck (Calamo et al., 2025), with the 8-record reference archive of Chen et al. (Chen et al., 2023a,b), enabling the evaluation of automated domain modeling approaches across different levels of complexity and scale. Given a natural language description, the task is to generate a corresponding domain model. For each description, a reference domain model is provided as ground truth. A metric is used to compare the generated domain model with the corresponding ground-truth model. To demonstrate the utility of the benchmark, we evaluate multiple automated domain modeling approaches, including heuristic rule-based methods and LLM-driven strategies. In accordance with the FAIR4RS recommendations (Chue Hong et al., 2022), the benchmark is provided as a research artifact to encourage reuse and support future research on automated domain modeling.
