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Automatic Requirement Dependency Extraction Based on Integrated Active Learning Strategies

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Abstract

Since requirement dependency extraction is a cognitively challenging and error-prone task, this paper proposes an automatic requirement dependency extraction method based on integrated active learning strategies. In this paper, the coefficient of variation method was used to determine the corresponding weight of the impact factors from three different angles: uncertainty probability, text similarity difference degree and active learning variant prediction divergence degree. By combining the three factors with the proposed calculation formula to measure the information value of dependency pairs, the top K dependency pairs with the highest comprehensive evaluation value are selected as the optimal samples. As the optimal samples are continuously added into the initial training set, the performance of the active learning model using different dependency features for requirement dependency extraction is rapidly improved. Therefore, compared with other active learning strategies, a higher evaluation measure of requirement dependency extraction can be achieved by using the same number of samples. Finally, the proposed method using the PV-DM dependency feature improves the weight-F1 by 2.71%, the weight-recall by 2.45%, and the weight-precision by 2.64% in comparison with other strategies, saving approximately 46% of the labelled data compared with the machine learning approach.

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Acknowledgements

This work was supported by the Scientific Research Funding Project of Education Department of Liaoning Province 2021, China (No. LJKZ0434).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang, 110142, China

    Hui Guan, Guorong Cai & Hang Xu

  2. Key Laboratory of Industrial Intelligence Technology on Chemical Process, Shenyang, 110142, China

    Hui Guan

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  1. Hui Guan
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  3. Hang Xu
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Correspondence to Hui Guan.

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The authors declared that they have no conflicts of interest to this work.

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Colored figures are available in the online version at https://link.springer.com/journal/11633

Hui Guan received the B. Sc. and M. Sc. degrees in computer science from Shenyang Institute of Chemical Technology, China in 2000 and 2006, respectively, and the Ph. D. degree in software engineering from De Montfort University, UK in 2014. She is currently an associated professor at School of Computer Science and Technology, Shenyang University of Chemical Technology, China.

Her research interests include software evolution, semantic computing and software requirement engineering.

E-mail: h.guan@syuct.edu.cn (Corresponding author)

ORCID iD: 0000-0003-4456-6674

Guorong Cai received the B. Sc. degree in software engineering from Jiangxi Normal University, China in 2020. He is now a master student at Shenyang University of Chemical Technology, China.

His research interests include active learning, machine learning and data mining.

E-mail: 1456177729@qq.com

Hang Xu received the B. Sc. degree in applied physics from Suzhou University of Science and Technology, China in 2020. He is now a master student at Shenyang University of Chemical Technology, China.

His research interests include ensemble learning, machine learning, and data mining.

E-mail: 1974139682@qq.com

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Guan, H., Cai, G. & Xu, H. Automatic Requirement Dependency Extraction Based on Integrated Active Learning Strategies. Mach. Intell. Res. (2024). https://doi.org/10.1007/s11633-023-1420-1

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