Facilitating reuse in model-based development with context-dependent model element recommendations

Reuse recommendation systems suggest code entities useful for the task at hand within the IDE. Current approaches focus on code-based development. However, model-based development poses similar challenges to developers regarding the identification of useful elements in large and complex reusable modeling libraries. This paper proposes an approach for recommending library elements for domain specific languages. We instantiate the approach for Simulink models and evaluate it by recommending library blocks for a body of 165 Simulink files from a public repository. We compare two alternative variants for computing recommendations: association rules and collaborative filtering. Our results indicate that the collaborative filtering approach performs better and produces recommendations for Simulink models with satisfactory precision and recall.