Mining Crosscutting Concerns through Random Walks

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.

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