Finding with NEMO: a recommender system to forecast the next modeling operations

Abstract

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 NEMO, a recommender system based on an Encoder-Decoder neural network to assist modelers in performing model editing operations. NEMO learns from past modeling activities and performs predictions employing a deep learning technique. Such an algorithm has been successfully applied in machine translation to convert a text from a language to another foreign language and vice versa. An empirical evaluation on a dataset of BPMN change-based persistent model demonstrates that the technique permits learning from existing operations and effectively predicting the next editing operations with considerably high prediction accuracy. In particular, NEMO gets 0.977 as precision/recall and 0.992 as success rate score by the best performance.