Supervised Classification of UML Class Diagrams Based on F-KNB

Abstract

Often most software development doesn’t start from scratch but applies previously developed artifacts. These reusable artifacts are involved in various phases of the software life cycle, ranging from requirements to maintenance. Software design as the high level of software development process has an important impact on the following stages, so its reuse is gaining more and more attention. Unified modeling language (UML) class diagram as a modeling tool has become a de facto standard of software design, and thus its reuse also becomes a concern accordingly. So far, the related research on the reuse of UML class diagrams has focused on matching and retrieval. As a large number of class diagrams enter the repository for reuse, classification has become an essential task. The classification is divided into unsupervised classification (also known as clustering) and supervised classification. In our previous work, we discussed the clustering of UML class diagrams. In this paper, we focus on only the supervised classification of UML class diagrams and propose a supervised classification method. A novel ensemble classifier F-KNB combining both dependent and independent construction ideas is built. The similarity of class diagrams is described, in which the semantic, structural and hybrid matching is defined, respectively. The extracted feature elements are used in base classifiers F-KNN and F-NBs that are constructed based on improved K-nearest neighbors (KNNs) and Naive Bayes (NB), respectively. A series of experimental results show that the proposed ensemble classifier F-KNB shows a good classification quality and efficiency under the condition of variable size and distribution of training samples.