A partitional clustering algorithm for crosscutting concerns identification
SEPADS'09 Proceedings of the 8th WSEAS International Conference on Software engineering, parallel and distributed systems
A role-based crosscutting concerns mining approach to evolve Java systems towards AOP
Proceedings of the joint international and annual ERCIM workshops on Principles of software evolution (IWPSE) and software evolution (Evol) workshops
A new genetic clustering based approach in aspect mining
MMACTEE'06 Proceedings of the 8th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering
Construction and analysis of vector space models for use in aspect mining
Proceedings of the 50th Annual Southeast Regional Conference
Feature selection for clustering based aspect mining
Proceedings of the 4th international workshop on Variability & composition
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Clustering is a division of data into groups of similar objects. Aspect mining is a process that tries to identify cross-cutting concerns in existing software systems. The goal is to refactor the existing systems to use aspect oriented programming, in order to make them easier to maintain and to evolve. This paper aims at presenting a new k-means based clustering algorithm used in Aspect Mining. Clustering is used in order to identify crosscutting concerns. We propose some quality measures in order to evaluate the results both from the clustering point of view and the aspect mining point of view, and we also report two case studies.