Efficiently mining crosscutting concerns through random walks

Inspired by our past manual aspect mining experiences, this paper describes a random walk model to approximate how crosscutting concerns can be discovered in the absence of domain knowledge of the investigated application. Random walks are performed on the coupling graphs extracted from the program sources. The ideas underlying the popular page-rank algorithm are adapted and extended to generate ranks reflecting the degrees of "popularity" and "significance" for each of the program elements on the coupling graphs. Filtering techniques, exploiting both types of ranks, are applied to produce a final list of candidates representing crosscutting concerns. The resulting aspect mining algorithm is evaluated on numerous Java applications ranging from a small-scale drawing application, to a medium-sized middleware application, and to a largescale enterprise application server. In seconds, the aspect mining algorithm is able to produce results comparable to our prior manual mining efforts. The mining algorithm also proves effective in helping domain experts identify latent crosscutting concerns.