在使用预训练的语言模型的建模活动期间推荐元模型概念
Recommending Metamodel Concepts during Modeling Activities with Pre-Trained Language Models
摘要：
在模型驱动工程中，设计概念上合理的元模型（包含与应用程序领域相关的正确语义）是特别乏味的。由于元模型定义了领域概念之间的复杂关系，因此建模者在与应用程序领域保持一致的同时彻底定义这些概念是至关重要的。我们提出了一种方法，以协助建模者在元模型的设计，推荐相关领域的概念，在几个建模方案。我们的方法不需要从域中提取知识或手工设计完成规则。相反，我们设计了一个完全数据驱动的方法，使用深度学习模型，能够抽象领域概念，从结构和词汇元模型的属性在数千个独立的元模型的语料库中学习。我们在包含166个元模型的测试集上评估了我们的方法，这些元模型在模型训练过程中看不到，测试样本超过5000个。我们的初步结果表明，训练模型能够提供准确的顶部$5$列表的相关建议的概念重命名的情况下。虽然很有希望，但是对于元模型的迭代构造场景，结果不那么令人信服，部分原因是我们使用保守的策略来评估建议。
英文摘要：
The design of conceptually sound metamodels that embody proper semantics in relation to the application domain is particularly tedious in Model-Driven Engineering. As metamodels define complex relationships between domain concepts, it is crucial for a modeler to define these concepts thoroughly while being consistent with respect to the application domain. We propose an approach to assist a modeler in the design of a metamodel by recommending relevant domain concepts in several modeling scenarios. Our approach does not require to extract knowledge from the domain or to hand-design completion rules. Instead, we design a fully data-driven approach using a deep learning model that is able to abstract domain concepts by learning from both structural and lexical metamodel properties in a corpus of thousands of independent metamodels. We evaluate our approach on a test set containing 166 metamodels, unseen during the model training, with more than 5000 test samples. Our preliminary results show that the trained model is able to provide accurate top-$5$ lists of relevant recommendations for concept renaming scenarios. Although promising, the results are less compelling for the scenario of the iterative construction of the metamodel, in part because of the conservative strategy we use to evaluate the recommendations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
