Improved Hierarchical Clustering Algorithm for Software Architecture Recovery

Recovering software architecture from a software system is a good manner to understand and maintain it, and has great significance for legacy systems whose problem is a lack of information for maintenance and evolution. Recently, many clustering algorithm have been employed for software architecture recovery. To increase the recovering accuracy and enhance the effectivity, an improved hierarchical clustering algorithm is proposed in this paper. On the basis of ScaLable Information BOttleneck (LIMBO) algorithm, our methodology is achieved by introducing more static and dynamic information as the features of a software system and moreover different weights are assigned to different features. With the help of labels generated during clustering, evaluation is achieved. Finally, some experiments are conducted, and the experimental results depict our proposed algorithm improves the accuracy and efficiency of software architecture recovery to some extend.