中圖分類號： TP183
文獻(xiàn)標(biāo)識碼： A
DOI： 10.19358/j.issn.2096-5133.2022.02.007
引用格式： 羅賢昌,，薛吟興. 基于BERT的提示學(xué)習(xí)實現(xiàn)軟件需求精確分類[J].信息技術(shù)與網(wǎng)絡(luò)安全,，2022，41(2)：39-45.

Accurately classify software requirements using prompt learning on BERT

Abstract： Software requirement is a direct feedback from users to software utility. The accurate classification of software requirements engineering can greatly reduce maintenance costs and significantly speed up the process of software development and maintenance. Traditional machine learning-based classification methods(such as logistic regression, support vector machines, and K-nearest neighbor algorithms) or simply applying BERT(Bidirectional Encoder Representation from Transformers) models cannot learn to make the most use of the PROMISE data set for software requirements, and ultimately appear to be poor generalization or low classification efficiency. In order to enhance the BERT model′s ability to understand the semantics of natural language texts, this paper applies the idea of prompt learning to transform the K classification selection problem into a binary judgment problem. The experimental results show that there is no need to implement a sample equalization strategy for unbalanced data sets. The classification performance of this model is far superior to the above two classification tasks, and the best prediction results are finally obtained.

Key words : software requirement,；accurately classify；bidirectional encoder representation from transformer,；prompt learning