A Comparative Classification of Aspect Mining Approaches
Bounour Nora, Ghoul Said & Atil Fadila
Journal of Computer Science, Volume 2, Issue 4, pp. 322–325 · doi.org/10.3844/jcssp.2006.322.325
Abstract
In object oriented paradigm, 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. Identifying such code automatically greatly improves both the maintainability and the evolveability of the application. Aspect mining aims to identify crosscutting concerns in existing systems, thereby improving the system's comprehensibility and enabling migration of existing (object-oriented) programs to aspect-oriented ones. Aspect are mined either by use of static information or dynamic information of the code. The purpose of this article is to present a survey of the current techniques of aspect mining. We seek to understand both the strengths and limitations of this new area.
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Nora, B., Said, G. & Fadila, A. (2006). A Comparative Classification of Aspect Mining Approaches. Journal of Computer Science, 2(4), 322–325. https://doi.org/10.3844/jcssp.2006.322.325
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