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2008
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Automated Aspect Recommendation through Clustering-Based Fan-in Analysis

8 years 11 months ago
Automated Aspect Recommendation through Clustering-Based Fan-in Analysis
Identifying code implementing a crosscutting concern (CCC) automatically can benefit the maintainability and evolvability of the application. Although many approaches have been proposed to identify potential aspects, a lot of manual work is typically required before these candidates can be converted into refactorable aspects. In this paper, we propose a new aspect mining approach, called Clustering-Based Fan-in Analysis (CBFA), to recommend aspect candidates in the form of method clusters, instead of single methods. CBFA uses a new lexical based clustering approach to identify method clusters and rank the clusters using a new ranking metric called cluster fanin. Experiments on Linux and JHotDraw show that CBFA can provide accurate recommendations while improving aspect mining coverage significantly compared to other state-of-the-art mining approaches.
Danfeng Zhang, Yao Guo, Xiangqun Chen
Added 31 May 2010
Updated 31 May 2010
Type Conference
Year 2008
Where KBSE
Authors Danfeng Zhang, Yao Guo, Xiangqun Chen
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