Improving the Condensing of Reverse Engineered Class Diagrams Using Weighted Network Metrics

Reverse engineered class diagrams (REDs) are helpful to ease the comprehension of complex software. However, the original REDs might contain many details and thus provide little benefit. Condensing REDs by identifying the most important classes (aka key classes) and discarding unimportant ones has been regarded as a promising way. In the last decade, many key class prediction (KCP) approaches have been proposed. However, the unweighted network metrics used in these studies fail to capture the dependency strength between classes and thus cannot precisely measure the actual complexity of classes. In this paper, we propose an approach, KEEPER, for condensing REDs, which introduces a set of weighted network metrics to characterize the complexity of classes and to build KCP models. Empirical results show that KEEPER performs better than all the baseline approaches and has a good scalability.