Katholieke Universiteit Leuven
Software patterns are widely adopted 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. In this paper, the authors argue that the relational nature of both software models and software patterns can be exploited to automate this cumbersome procedure. First, they propose a novel approach to selecting applicable software patterns, which requires only little interaction with a software developer. Second, they discuss how relational learning can be used to further automate this semi-automated approach.