Reuse and Automated Integration of Recommenders for Modelling Languages

Abstract

Many recommenders for modelling tasks have recently appeared. They use a variety of recommendation methods,tailored to concrete modelling languages. Typically, recommenders are created as independent programs, and subsequently need to be integrated within a modelling tool, incurring in high development effort. Moreover, it is currently not possible to reuse a recommender created for a modelling language with a different notation, even if they are similar. To attack these problems, we propose a methodology to reuse and integrate recommenders into modelling tools. It considers four orthogonal dimensions: the target modelling language, the tool, the recommendation source, and the recommended items. To make homogeneous the access to arbitrary recommenders, we propose a reference recommendation service that enables indexing recommenders, investigating their properties, and obtaining recommendations likely coming from several sources. Our methodology is supported by IronMan, an Eclipse plugin that automates the integration of recommenders within Sirius and tree-based editors, and can bridge recommenders created for a modelling language for their reuse with a different one. We evaluate the power of the tool by reusing 2 recommenders for 4 different languages, and integrating them into 6 modelling tools.