Recovery and Analysis of Architecture Descriptions using Centrality Measures
This addresses the need for explicit architecture descriptions in software systems to aid communication and maintenance, though it appears incremental as it builds on existing centrality measures and layered architecture styles.
The paper tackles the problem of recovering software architecture descriptions by applying centrality measures from Social Network Analysis to program elements, then assigning them to layers using either predefined rules or learned rules from labeled data, with both approaches evaluated.
The necessity of an explicit architecture description has been continuously emphasized to communicate the system functionality and for system maintenance activities. This paper presents an approach to extract architecture descriptions using the {\em centrality measures} from the theory of Social Network Analysis. The architecture recovery approach presented in this paper works in two phases. The first phase aims to calculate centrality measures for each program element in the system. The second phase assumes that the system has been designed around the layered architecture style and assigns layers to each program element. Two techniques to assign program elements are presented. The first technique of layer assignment uses a set of pre-defined rules, while the second technique learns the rules of assignment from a pre-labelled data set. The paper presents the evaluation of both approaches.