Software Clustering based on Omnipresent Object Detection
Zhihua Wen, Vassilios Tzerpos · 2005
The detection of omnipresent objects can be an important aid to the process of understanding a large software system. As a result, various detection techniques have been presented in the literature. However, these techniques do not take the subsystem structure into account when deciding whether an object is omnipresent or not. In this paper, we present a new set of detection methods for omnipresent objects that maintain that an object needs to be connected to a large number of subsystems before it is deemed omnipresent. We compare this novel approach to existing ones. We also introduce a framework that can improve the effectiveness of existing software clustering algorithms by combining them with an omnipresent object detection method. Experiments with two large software systems demonstrate the usefulness of this framework.