一句话：提出对抗域适应方法RADIATION，在无目标域标注样本下实现跨域需求链接，F1显著超越基线。
需求链接对软件维护与演化至关重要，但实际中链接缺失或错误标注，重建耗时易错。现有学习方法在标注样本稀少的冷启动项目上失效。为此，论文提出RADIATION，一种基于对抗的域适应方法：先用基于IDF的掩码策略过滤域特有特征，再在源域用充足标注样本预训练链接模型，并通过距离增强的对抗技术适配到目标域，且不使用任何目标域标注样本。在五个公开数据集上，RADIATION达到66.4%精确率、89.2%召回率，F1较最优基线提升13.4%–42.9%；消融显示IDF掩码与距离增强损失均显著有效。
Requirements linking is the core of software system maintenance and evolution, and it is critical to assuring software quality. In practice, however, the requirements links are frequently absent or incorrectly labeled, and reconstructing such ties is time-consuming and error-prone. Numerous learning-based approaches have been put forth to address the problem. However, these approaches will lose effectiveness for the Cold-Start projects with few labeled samples. To this end, we propose RADIATION, an adversarial-based domain adaptation approach for cross-domain requirements linking. Generally, RADIATION firstly adopts an IDF-based Masking strategy to filter the domain-specific features. Then it pre-trains a linking model in the source domain with sufficient labeled samples and adapts the model to target domains using a distance-enhanced adversarial technique without using any labeled target samples. Evaluation on five public datasets shows that RADIATION could achieve 66.4% precision, 89.2% recall, and significantly outperform state-of-the-art baselines by 13.4% -42.9% F1. In addition, the designed components, i.e., IDF-based Masking and Distance-enhanced Loss, could significantly improve performance.
@inproceedings{ChangLWLW23,
title = {Cross-Domain Requirements Linking via Adversarial-based Domain Adaptation},
author = {Zhiyuan Chang and Mingyang Li and Qing Wang and Shoubin Li and Junjie Wang},
booktitle = {ICSE},
year = {2023},
doi = {10.1109/icse48619.2023.00138}
}