PFN: A novel program feature network for program comprehension
Xiangyue Liu, Xiaobing Sun, Bin Li, Junwu Zhu · 2014
Program comprehension is one of the most frequently performed activities during software maintenance and evolution. In order to facilitate program comprehension, a variety of graphical models have been proposed in software engineering community to construct relationships between program elements. These graphical models are mostly used for understanding the system based on structural syntax dependencies between program elements. However, these graphical models fail to extract the functional or semantic features of the system. Thus, developers still cannot effectively identify the functional part in source code fit for their needs. This paper tries to fill this gap, and proposes a novel representation, program feature network (PFN), to identify the semantic features of the program at class level. PFN is generated based on the relational topic model, a hierarchical probabilistic model of networks. Based on PFN, the semantic features and the links between pairs of two classes in the program can be clearly shown. In addition, PFN can predict the possible links between the newly change request in existing program feature network rather than reconstructing the representation from the start.