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Automatic traceability link recovery via active learning Research Articles

Tian-bao Du, Guo-hua Shen, Zhi-qiu Huang, Yao-shen Yu, De-xiang Wu,tbdu_312@outlook.com,ghshen@nuaa.edu.cn,zqhuang@nuaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 8, Pages 1217-1225 doi: 10.1631/FITEE.1900222

Abstract: (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 , we propose a new TLR approach based on (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.

Keywords： Automatic Traceability link recovery Manpower Active learning