Automated Aspect Recommendation through Clustering-Based Fan-in Analysis
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
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Code implementing a crosscutting concern spreads over many parts of the Linux code. Identifying these code automatically can benefit both the maintainability and evolvability of Linux. In this paper, we present a case study on how to identify aspects in the Linux code. First, we analyze four typical crosscutting concerns in Linux and show how to apply existing mining approaches to identify these concerns. We then propose three new mining approaches and compare their performance with the original methods. Experiments show that the proposed mining approaches can find these concerns more efficiently in Linux.