Recovering Semantic Traceability between Requirements and Source Code Using Feature Representation Techniques

Requirement traceability is essential for software development and maintenance, thereby effectively recovering the requirements traceability has become an important issue for requirement engineering. With the development of software systems, it is always unrealistic to maintain traceability links between requirements and source code manually. Therefore, researchers have proposed information retrieval-based approaches to recover the links automatically. Although these methods reduce human labor, they do not fully extract the specific features, resulting in poor traceability accuracy. In this paper, we propose an approach to recovering traceability between requirements and source code, which combines word embedding and self-attention model to extract features and generate text vectors. These technologies make full use of the semantic information of the context and feature representation. In addition, the paper discusses the impact of code content and comments on the results and improves the results on weight. Finally, the proposed approach is compared with the commonly-used baselines, and the study results show that the proposed approach outperforms others.