Towards an automated pattern selection procedure in software models

Software patterns are a widely adopted technique to manage the rapidly increasing complexity of software. Despite their popularity, applying software patterns in a software model remains a time-consuming and error-prone manual task. This paper proposes a novel approach to the automated selection of applicable patterns. We argue that software models and patterns are relational, and propose using relational learning to assign general roles to software model elements, which are being used to select the most appropriate patterns. Furthermore, our approach provides hints on how to instantiate these patterns in the software model.status: publishe