Using Graph Embedding to Improve Requirements Traceability Recovery

Abstract

Information retrieval (IR) is widely used in automatically requirements traceability recovery. Corresponding approaches are built based on textual similarity, that is, the higher the similarity, the higher possibility of artifacts related. A common work of many IR-based techniques is to remove false positive links in the candidate links to achieve higher accuracy. In fact, traceability links can be recovered by different kinds of information, not only the textual information. In our study, we propose to recover more traceability links by exploring both textual features and structural information. Specifically, we use combined IR techniques to process the textual information of the software artifacts, and extract the structural information from the source code, establishing corresponding code relationship graphs. We then incorporate such structural information into the traceability recovery analysis by using graph embedding. The results show that combined IR techniques and using graph embedding technology to process structural information can improve the recovery traceability.