MLTracer: An Approach Based on Multi-Layered Gradient Boosting Decision Trees for Requirements Traceability Recovery
Xingfu Li, Bangchao Wang, Hongyan Wan, Yuanbang Li, Han Jiang, Yang Deng, Zhiyuan Zou · 2024
In recent years, increasing machine learning has been applied to Requirements Traceability Recovery (RTR). The performance of these approaches is not satisfactory. Besides, most of them perform well in recovering traceability links between specific artifacts but cannot maintain consistent performance in different scenarios. To alleviate this problem, we propose a novel approach based on Multi-Layered Gradient Boosting Decision Trees (mGBDT) for RTR which is called MLTracer. MLTracer stacks several GBDTs and learns hierarchical representations of artifact link features. Through layer-by-layer training, it adapts to the feature distribution in different scenarios to improve generalization ability. MLTracer is evaluated on five software projects and compared with six state-of-the-art approaches. The results indicate that MLTracer achieves an average F1 score of 0.6153, outperforming six baseline approaches across all datasets. It proves that MLTracer achieves state-of-the-art performance and has strong applicability in actual industry.