Automatically classifying non-functional requirements using deep neural network
作者：
Highlights：
• The improved word embedding model helps to facilitate adequate-ly representation learning in pre-training.
• The improved dropout method can reduce the number of iterations needed for training, accelerate the training of deep neural networks.
• We expanded the original non-functional requirements dataset (PROMISE dataset) and designed a new dataset called SOFTWARE NFR.
• This model can be applied in the automation of software engineering.
摘要
•The improved word embedding model helps to facilitate adequate-ly representation learning in pre-training.•The improved dropout method can reduce the number of iterations needed for training, accelerate the training of deep neural networks.•We expanded the original non-functional requirements dataset (PROMISE dataset) and designed a new dataset called SOFTWARE NFR.•This model can be applied in the automation of software engineering.
论文关键词：Non-functional requirements,Non-functional requirements classification,BERT,N-gram,Bi-LSTM,Multi-sample dropout
论文评审过程：Received 19 October 2021, Revised 21 June 2022, Accepted 25 July 2022, Available online 26 July 2022, Version of Record 2 August 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.108948
