GEA: A Goal-Driven Approach toDiscovering Early Aspects

Abstract

Aspect-oriented software development has become an important development and maintenance approach to software engineering across requirements, design and implementation phases. However, discovering early aspects from requirements for a better integration of crosscutting concerns into a target system is still not well addressed in the existing works. In this paper, we propose a Goal-driven Early Aspect approach (called GEA) to discovering early aspects by means of a clustering algorithm in which relationships among goals and use cases are utilized to explore similarity degrees of clustering goals, and total interaction degrees are devised to check the validity of the formation of each cluster. Introducing early aspects not only enhances the goal-driven requirements modeling to manage crosscutting concerns, but also provides modularity insights into the analysis and design of software development. Moreover, relationships among goals represented numerically are more informative to discover early aspects and more easily to be processed computationally than qualitative terms. The proposed approach is illustrated by using two problem domains: a meeting scheduler system and a course enrollment system. An experiment is also conducted to evaluate the benefits of the proposed approach with Mann-Whitney U-test to show that the difference between with GEA and without GEA is statistically significant.