Enhancing requirements-to-code traceability with GA-XWCoDe: Integrating XGBoost, Node2Vec, and genetic algorithms for improving model performance and stability
Zhiyuan Zou, Bangchao Wang, Xinrong Hu, Yang Deng, Hongyan Wan, Huan Jin
Abstract
This study addresses the challenge of requirements-to-code traceability by proposing a novel model, Genetic Algorithm-XGBoost With Code Dependency (GA-XWCoDe), which integrates eXtreme Gradient Boosting (XGBoost) with a Node2Vec model-weighted code dependency strategy and genetic algorithms for parameter optimisation. XGBoost mitigates overfitting and enhances model stability, while Node2Vec improves prediction accuracy for low-confidence links. Genetic algorithms are employed to optimise model parameters efficiently, reducing the resource intensity of traditional methods. Experimental results show that GA-XWCoDe outperforms the state-of-the-art method TRAceability lInk cLassifier (TRAIL) by 17.44% and Deep Forest for Requirement traceability (DF4RT) by 33.36% in terms of average F1 performance across four datasets. It is significantly superior to all baseline methods at a confidence level of α ¡0.01 and demonstrates exceptional performance and stability across various training data scales. • XGBoost with code dependencies and genetic algorithms for automated traceability. • Genetic algorithms automate parameter setup for XGBoost and code dependencies. • Code dependencies adjust low-confidence XGBoost links, surpassing SOTA performance. • Node2Vec applied to code dependency weights, greatly enhancing strategy performance.