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Automated aspect recommendation through clustering-based fan-in analysis

Publication ,  Conference
Zhang, D; Guo, Y; Chen, X
Published in: ASE 2008 - 23rd IEEE/ACM International Conference on Automated Software Engineering, Proceedings
November 21, 2008

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. © 2008 IEEE.

Duke Scholars

Published In

ASE 2008 - 23rd IEEE/ACM International Conference on Automated Software Engineering, Proceedings

DOI

Publication Date

November 21, 2008

Start / End Page

278 / 287
 

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Zhang, D., Guo, Y., & Chen, X. (2008). Automated aspect recommendation through clustering-based fan-in analysis. In ASE 2008 - 23rd IEEE/ACM International Conference on Automated Software Engineering, Proceedings (pp. 278–287). https://doi.org/10.1109/ASE.2008.38
Zhang, D., Y. Guo, and X. Chen. “Automated aspect recommendation through clustering-based fan-in analysis.” In ASE 2008 - 23rd IEEE/ACM International Conference on Automated Software Engineering, Proceedings, 278–87, 2008. https://doi.org/10.1109/ASE.2008.38.
Zhang D, Guo Y, Chen X. Automated aspect recommendation through clustering-based fan-in analysis. In: ASE 2008 - 23rd IEEE/ACM International Conference on Automated Software Engineering, Proceedings. 2008. p. 278–87.
Zhang, D., et al. “Automated aspect recommendation through clustering-based fan-in analysis.” ASE 2008 - 23rd IEEE/ACM International Conference on Automated Software Engineering, Proceedings, 2008, pp. 278–87. Scopus, doi:10.1109/ASE.2008.38.
Zhang D, Guo Y, Chen X. Automated aspect recommendation through clustering-based fan-in analysis. ASE 2008 - 23rd IEEE/ACM International Conference on Automated Software Engineering, Proceedings. 2008. p. 278–287.

Published In

ASE 2008 - 23rd IEEE/ACM International Conference on Automated Software Engineering, Proceedings

DOI

Publication Date

November 21, 2008

Start / End Page

278 / 287