Issue No. 05 - Sept.-Oct. (2012 vol. 38)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TSE.2011.83
Charles Zhang , The Hong Kong University of Science and Technology, Hong Kong
Hans-Arno Jacobsen , University of Toronto, Toronto
Inspired by our past manual aspect mining experiences, this paper describes a probabilistic random walk model to approximate the process of discovering crosscutting concerns (CCs) in the absence of the domain knowledge about the investigated application. The random walks are performed on the concept graphs extracted from the program sources to calculate metrics of “utilization” and “aggregation” for each of the program elements. We rank all the program elements based on these metrics and use a threshold to produce a set of candidates that represent crosscutting concerns. We implemented the algorithm as the Prism CC miner (PCM) and evaluated PCM on Java applications ranging from a small-scale drawing application to a medium-sized middleware application and to a large-scale enterprise application server. Our quantification shows that PCM is able to produce comparable results (95 percent accuracy for the top 125 candidates) with respect to the manual mining effort. PCM is also significantly more effective as compared to the conventional approach.
Phase change materials, Radiation detectors, Data mining, Manuals, Mathematical model, Computational modeling, Algorithm design and analysis, mining crosscutting concerns, Aspect mining
H. Jacobsen and C. Zhang, "Mining Crosscutting Concerns through Random Walks," in IEEE Transactions on Software Engineering, vol. 38, no. , pp. 1123-1137, 2012.