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2020, Volume 21, Issue 8

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

Automatic traceability link recovery via active learning

Affiliation(s): College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, protectauthorcritfootnotesize Nanjing 211106, China; Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210093, China; Key Laboratory of Safety-Critical Software, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; less

Received: 2019-05-02 Accepted: 2020-08-10 Available online: 2020-08-10
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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.

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