Self-organizing maps
Data mining: concepts and techniques
Data mining: concepts and techniques
Metrics and Models in Software Quality Engineering
Metrics and Models in Software Quality Engineering
AspectJ in Action: Practical Aspect-Oriented Programming
AspectJ in Action: Practical Aspect-Oriented Programming
Aspect Mining Using Event Traces
Proceedings of the 19th IEEE international conference on Automated software engineering
Aspect Mining through the Formal Concept Analysis of Execution Traces
WCRE '04 Proceedings of the 11th Working Conference on Reverse Engineering
Identifying Aspects Using Fan-In Analysis
WCRE '04 Proceedings of the 11th Working Conference on Reverse Engineering
A New k-means Based Clustering Algorithm in Aspect Mining
SYNASC '06 Proceedings of the Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
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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This paper proposes a new heuristic algorithm for optimizing the set of features of clustering based aspect mining that aims at identifying code which is likely to implement a crosscutting concern. Given a set of features, our algorithm selects important ones for clustering by using self-organizing maps (SOM). We implemented the algorithm by using the SOM Toolbox and evaluated its impact by evaluating the accuracy of aspect mining based on the optimized set of features. The results of experiments revealed that different programs have different optimal features and showed following improvements: 1) the accuracy of clustering concerns are increased even the number of features are decreased. 2) our algorithm successfully find the optimal set of features automatically against different programs.