A Qualitative Comparison of Three Aspect Mining Techniques

Abstract

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 existing (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 mutually compare the individual results of each technique based on the discovered aspects and on the level of detail and quality 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.