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-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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Project supported by the National Natural Science Foundation of China (No. 61772270), the National Key Research and Development Project of China (Nos. 2016YFB1000802 and 2018YFB1003902), and the Funding of the Key Laboratory of Safety-Critical Software, China (No. 1015-XCA1816403)
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Tian-bao DU designed the research. Guo-hua SHEN drafted the manuscript. Zhi-qiu HUANG, Yao-shen YU, and De-xiang WU helped organized the manuscript. Tian-bao DU revised and finalized the paper.
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Tian-bao DU, Guo-hua SHEN, Zhi-qiu HUANG, Yao-shen YU, and De-xiang WU declare that they have no conflict of interest.
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Du, Tb., Shen, Gh., Huang, Zq. et al. Automatic traceability link recovery via active learning. Front Inform Technol Electron Eng 21, 1217–1225 (2020). https://doi.org/10.1631/FITEE.1900222
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DOI: https://doi.org/10.1631/FITEE.1900222