MemoRec: A Recommender System for Assisting Modelers in Specifying Metamodels

TL;DR

MemoRec is a recommender system that uses collaborative filtering to assist modelers in specifying accurate metamodels in Model Driven Engineering, reducing errors and improving modeling efficiency.

Contribution

It introduces a novel collaborative filtering approach for recommending metamodel entities, enhancing support during the metamodeling phase in MDE.

Findings

MemoRec achieves high success rate in recommendations.

It improves precision and recall in suggesting relevant metamodel elements.

The system effectively supports modelers with partial metamodels.

Abstract

Model Driven Engineering (MDE) has been widely applied in software development, aiming to facilitate the coordination among various stakeholders. Such a methodology allows for a more efficient and effective development process. Nevertheless, modeling is a strenuous activity that requires proper knowledge of components, attributes, and logic to reach the level of abstraction required by the application domain. In particular, metamodels play an important role in several paradigms, and specifying wrong entities or attributes in metamodels can negatively impact on the quality of the produced artifacts as well as other elements of the whole process. During the metamodeling phase, modelers can benefit from assistance to avoid mistakes, e.g., getting recommendations like meta-classes and structural features relevant to the metamodel being defined. However, suitable machinery is needed to mine data from repositories of existing modeling artifacts and compute recommendations. In this work, we propose MemoRec, a novel approach that makes use of a collaborative filtering strategy to recommend valuable entities related to the metamodel under construction. Our approach can provide suggestions related to both metaclasses and structured features that should be added in the metamodel under definition. We assess the quality of the work with respect to different metrics, i.e., success rate, precision, and recall. The results demonstrate that MemoRec is capable of suggesting relevant items given a partial metamodel and supporting modelers in their task.