Automatic traceability link recovery via active learning

Abstract

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-ofthe-art machine learning approach. The results indicate that the AL-based approach outperforms the other two approaches in terms of F-score.

Keywords

Automatic / Traceability link recovery / Manpower / Active learning

RIGHTS & PERMISSIONS

2020 Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature