A Qualitative Comparison of Three Aspect Mining Techniques

The fact that crosscutting concerns (aspects) cannot be well modularized in object oriented software is an impediment to program comprehension: the implementation of a concern is typically scattered over many locations and tangled with the implementation of other concerns, resulting in a system that is hard to explore and understand. Aspect mining aims to identify crosscutting concerns in a system, thereby improving the system’s comprehensibility and enabling migration of ex-isting (object-oriented) programs to aspect-oriented ones. In this paper, we compare three aspect mining techniques that were developed independently by different research teams: fan-in analysis, identifier analysis and dynamic analysis. We apply each technique to the same case (JHotDraw) and mu-tually compare the individual results of each technique based on the discovered aspects and on the level of detail and qual-ity of those aspects. Strengths, weaknesses and underlying assumptions of each technique are discussed, as well as their complementarity. We conclude with a discussion of possible ways to combine the techniques in order to achieve a better overall aspect-mining technique