A Hybrid Approach for Ripple Effect Analysis of Software Evolution Activities

Ripple effect is defined as the process of determining potential effects to a subject system resulting from a proposed software evolving activity. Since software engineers perform different evolving activities to respond to different kinds of requirements, ripple effect analysis has been globally recognized as a key factor of affecting the success of software evolution projects. The precision of most existing ripple effect analysis methods is not as good as expectation and lots of methods have their inherent limitations. This paper proposes a hybrid analysis method which combines the dynamic and information retrieval based techniques to support ripple effect analysis in source code. This combination is able to keep the feature of high recall rate of dynamic method and reduce the adverse effects of noise and analysis scope by the domain knowledge which is derived from source code by information retrieval technique. In order to verify the effectiveness of the proposed approach, we have performed the ripple effect analysis and compared the analysis results produced by dynamic, static, text, historical evolving knowledge based methods with the proposed method on one open source software named jEdit. The results show that the hybrid ripple effect analysis method, across several cut points, provides statistically significant improvements in both precision and recall rate over these techniques used independently.