XWCoDe: XGBoost with Weighted Code Dependency for Requirements-to-Code Traceability Link Recovery

Information Retrieval (IR), Machine Learning (ML), and Deep Learning (DL) have become mainstream methods for traceability link recovery. However, IR-based methods face the challenge of low precision, while DL-based methods require large-scale training data to achieve better performance. In this paper, we propose a novel model XWCoDe, which apply XGBoost combined with a weighted code dependency strategy to traceability link recovery domain. In order to refine the initial candidate links generated by the XGBoost model, the strategy only modifies low confidence candidate links and pioneers the use of graph embedding technology node2vec to calculate the importance of each code dependency relationship. The experimental results show that the average F1 score of XW CoDe on 4 datasets and 9 training/testing ratios is 12.93 % higher than the state-of-the-art method DF4RT.