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 alleviate these problems, we propose a methodology to reuse recommenders and integrate them into modelling tools. It considers four orthogonal dimensions: the target modelling language, the modelling tool, the recommendation source, and the recommended items. To make the access to arbitrary recommenders homogeneous, 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 modelling 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 modelling languages, and integrating them into 6 existing modelling tools.