Design Defect Detection Rules Generation: A Music Metaphor

We propose an automated approach for design defect detection. It exploits an algorithm that automatically finds rules for the detection of possible design defects, thus relieving the designer from doing so manually. Our algorithm derives rules in the form of metric/threshold combinations, from known instances of design defects (defect examples). Due to the large number of possible combinations, we use a music-inspired heuristic that finds the best harmony when combining metrics. We evaluated our approach on finding potential defects in three open-source systems (Xerces-J, Quick UML and Gantt). For all of them, we found more than 80% of known defects, a better result when compared to a state-of-the-art approach, where the detection rules are manually specified.