Traceability link recovery (TLR) is an important and costly software task that requires humans establish relationships between source and target artifact sets within the same project. Previous research has proposed to establish traceability links by machine learning approaches. However, current machine learning approaches cannot be well applied to projects without traceability information (links), because training an effective predictive model requires humans label too many traceability links. To save manpower, we propose a new TLR approach based on active learning (AL), which is called the AL-based approach. We evaluate the AL-based approach on seven commonly used traceability datasets and compare it with an information retrieval based approach and a state-of-the-art machine learning approach. The results indicate that the AL-based approach outperforms the other two approaches in terms of F-score.

可追溯性链接恢复（TLR）是一项重要且昂贵的软件任务，需要人工在同一项目中建立源工件集与目标工件集之间的关系。先前的研究建议通过机器学习方法来建立可追溯性链接。但是，当前的机器学习方法无法很好地应用于没有可追溯性信息（链接）的项目，因为训练有效的预测模型需要人类标记太多可追溯性链接。为了节省人力，我们提出了一种基于主动学习（AL）的新TLR方法，称为基于AL的方法。我们在七个常用的可跟踪数据集上评估了基于AL的方法，并将其与基于信息检索的方法和最新的机器学习方法进行了比较。