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.
翻译:设计在应用领域体现适当语义的、概念健全的元模型在模型-驱动工程中特别乏味。由于元模型界定了领域概念之间的复杂关系,因此模型家必须彻底界定这些概念,同时与应用领域保持一致。我们建议一种方法,通过在几个模型设想中推荐相关域概念,协助模型家设计元模型。我们的方法不需要从领域提取知识或手动设计完成规则。相反,我们设计一种完全由数据驱动的方法,采用深层次学习模型,通过在数千个独立的元模型中学习结构和词汇模型特性,从而能够抽象地确定领域概念。我们评估了一套测试集的方法,该测试集包含166个模型,在模型培训期间是看不见的,有5000多个测试样本。我们的初步结果显示,经过培训的模型能够提供准确的最多至5亿美元的有关概念重命名设想方案的建议清单。虽然很有希望,但结果对于迭代构建元模型的设想并不那么令人信服,部分是因为我们采用了保守的战略来评估建议。