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Sun 10 - Sat 16 October 2021
Thu 14 Oct 2021 23:20 - 23:40 at Room 1 - Machine learning and Recommender systems II Chair(s): Antonio Cicchetti

Nowadays, while modeling environments provide users with facilities to specify different kinds of artifacts, e.g., metamodels, models, and transformations, the possibility of learning from previous modeling experiences and being assisted during modeling tasks remains largely unexplored. In this paper, we propose MORGAN, a recommender system based on a graph neural network (GNN) to assist modelers in performing the specification of metamodels and models. The (meta)model being specified, and the training data are encoded in a graph-based format by exploiting natural language processing (NLP) techniques. Afterward, a graph kernel function uses the extracted graphs to provide modelers with relevant recommendations to complete the partially specified (meta)models. We evaluated MORGAN on real-world datasets using various quality metrics, i.e., precision, recall, and F-measure. The obtained results are encouraging and demonstrate the feasibility of our tool to support modelers while specifying metamodels and models.

Thu 14 Oct

Displayed time zone: Osaka, Sapporo, Tokyo change

23:00 - 00:00
Machine learning and Recommender systems IITechnical Papers at Room 1
Chair(s): Antonio Cicchetti Mälardalen University
Recommender Systems in Model-Driven Engineering: A Systematic Mapping ReviewJ1ST
Technical Papers
Lissette Almonte Universidad Autónoma de Madrid, Esther Guerra , Iván Cantador Universidad Autonoma de Madrid, Juan de Lara Autonomous University of Madrid
A GNN-based Recommender System to Assist the Specification of Metamodels and ModelsFT
Technical Papers
Juri Di Rocco University of L'Aquila, Claudio Di Sipio University of L'Aquila, Davide Di Ruscio University of L'Aquila, Phuong T. Nguyen University of L’Aquila
Towards Reinforcement Learning for In-Place Model TransformationsVISION
Technical Papers