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Model Driven Engineering (MDE) has been widely applied in softwaredevelopment, aiming to facilitate the coordination among various stakeholders.Such a methodology allows for a more efficient and effective developmentprocess. Nevertheless, modeling is a strenuous activity that requires properknowledge of components, attributes, and logic to reach the level ofabstraction required by the application domain. In particular, metamodels playan important role in several paradigms, and specifying wrong entities orattributes in metamodels can negatively impact on the quality of the producedartifacts as well as other elements of the whole process. During themetamodeling phase, modelers can benefit from assistance to avoid mistakes,e.g., getting recommendations like meta-classes and structural featuresrelevant to the metamodel being defined. However, suitable machinery is neededto mine data from repositories of existing modeling artifacts and computerecommendations. In this work, we propose MemoRec, a novel approach that makesuse of a collaborative filtering strategy to recommend valuable entitiesrelated to the metamodel under construction. Our approach can providesuggestions related to both metaclasses and structured features that should beadded in the metamodel under definition. We assess the quality of the work withrespect to different metrics, i.e., success rate, precision, and recall. Theresults demonstrate that MemoRec is capable of suggesting relevant items givena partial metamodel and supporting modelers in their task.

MemoRec: A Recommender System for Assisting Modelers in Specifying Metamodels