Automatic traceability link recovery via active learning  

作　　者：Tian-bao DU Guo-hua SHEN Zhi-qiu HUANG Yao-shen YU De-xiang WU 

机构地区：[1]College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China [2]Collaborative Innovation Center of Novel Software Technology and Industrialization,Nanjing 210093,China [3]Key Laboratory of Safety-Critical Software,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China

出　　处：《Frontiers of Information Technology & Electronic Engineering》2020年第8期1217-1225,共9页信息与电子工程前沿（英文版）

基　　金：the National Natural Science Foundation of China(No.61772270);the National Key Research and Development Project of China(Nos.2016YFB1000802 and2018YFB1003902);the Funding of the Key Laboratory of Safety-Critical Software,China(No.1015-XCA1816403)。

摘　　要：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.

关 键 词：AUTOMATIC Traceability link recovery MANPOWER Active learning 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]