Recommending Metamodel Concepts during Modeling Activities with Pre-Trained Language Models
This work addresses the problem of improving metamodel design efficiency for modelers in Model-Driven Engineering, but it is incremental as it builds on existing deep learning techniques with mixed results across scenarios.
The paper tackles the tedious task of designing semantically sound metamodels in Model-Driven Engineering by proposing a data-driven approach using pre-trained language models to recommend relevant domain concepts during modeling activities, achieving accurate top-5 recommendations for concept renaming scenarios on a test set of 166 metamodels with over 5000 samples.
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.