Identifying composite crosscutting concerns with scatter-based graph clustering

Identifying composite crosscutting concerns (CCs) is a research task and challenge of aspect mining. In this paper, we propose a scatter-based graph clustering approach to identify composite CCs. Inspired by the state-of-the-art link analysis techniques, we propose a two-state model to approximate how CCs tangle with core modules. According to this model, we obtain scatter and centralization scores for each program element. Especially, the scatter scores are adopted to select CC seeds. Furthermore, to identify composite CCs, we adopt a novel similarity measurement and develop an undirected graph clustering to group these seeds. Finally, we compare it with the previous work and illustrate its effectiveness in identifying composite CCs.