@inproceedings{30c559b8420343c588e9359bc98cae2c,
title = "NFRNet: A Deep Neural Network for Automatic Classification of Non-Functional Requirements",
abstract = "Non-functional requirements specify those qualities that software products must have in order to meet the user's business requirements. The elicitation of these non-functional requirements requires expertise, experience, and domain knowledge, which is challenging and time-consuming for requirements engineers and developers. It would be very beneficial if the nonfunctional requirements can be automatically extracted from the requirements documentation to reduce the human efforts, time, and avoid the mental fatigue. In this paper, we present a novel deep neural network model called NFRNet to automatically extract non-functional requirements from software requirements documentation.",
keywords = "BERT, Bi-LSTM, Multi-sample dropout, N-gram, Non-functional requirements",
author = "Bing Li and Zhi Li and Yilong Yang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 29th IEEE International Requirements Engineering Conference, RE 2021 ; Conference date: 20-09-2021 Through 24-09-2021",
year = "2021",
doi = "10.1109/RE51729.2021.00057",
language = "英语",
series = "Proceedings of the IEEE International Conference on Requirements Engineering",
publisher = "IEEE Computer Society",
pages = "434--435",
editor = "Ana Moreira and Kurt Schneider and Michael Vierhauser and Jane Cleland-Huang",
booktitle = "Proceedings - 29th IEEE International Requirements Engineering Conference, RE 2021",
address = "美国",
}