一句话：提出 PRCBERT，用灵活提示模板与 BERT 预训练语言模型实现需求分类，性能优于 NoRBERT。
需求分类是需求工程中的长期重要问题，已有工作多采用 SVM、决策树等传统机器学习方法，近期最先进的 NoRBERT 借助预训练语言模型 BERT 取得了较好效果，但其使用的 PROMISE 数据集仅含数百条需求且内容已过时，所用 NLP 技术也可能落后。本文提出基于 BERT 类预训练语言模型的提示学习需求分类方法 PRCBERT，通过设计灵活的提示模板实现准确分类。作者在 PROMISE、NFR-Review 两个小规模数据集以及自建的大规模需求数据集 NFR-SO 上开展实验，结果表明 PRCBERT 的分类性能优于 NoRBERT 和采用标准提示模板的 MLM-BERT。此外，在去标签的 NFR-Review 与 NFR-SO 数据集上，在 PROMISE 上微调得到的 Trans_PRCBERT 结合自学习策略后，分别取得 53.27% 与 72.96% 的 F1 值，展现出可接受的零样本性能。
Software requirement classification is a longstanding and important problem in requirement engineering. Previous studies have applied various machine learning techniques for this problem, including Support Vector Machine (SVM) and decision trees. With the recent popularity of NLP technique, the state-of-the-art approach NoRBERT utilizes the pre-trained language model BERT and achieves a satisfactory performance. However, the dataset PROMISE used by the existing approaches for this problem consists of only hundreds of requirements that are outdated according to today’s technology and market trends. Besides, the NLP technique applied in these approaches might be obsolete. In this paper, we propose an approach of prompt learning for requirement classification using BERT-based pretrained language models (PRCBERT), which applies flexible prompt templates to achieve accurate requirements classification. Experiments conducted on two existing small-size requirement datasets (PROMISE and NFR-Review) and our collected large-scale requirement dataset NFR-SO prove that PRCBERT exhibits moderately better classification performance than NoRBERT and MLM-BERT (BERT with the standard prompt template). On the de-labeled NFR-Review and NFR-SO datasets, Trans_PRCBERT (the version of PRCBERT which is fine-tuned on PROMISE) is able to have a satisfactory zero-shot performance with 53.27% and 72.96% F1-score when enabling a self-learning strategy.
@inproceedings{paperbot7272,
title = {PRCBERT: Prompt Learning for Requirement Classification using BERT-based Pretrained Language Models},
author = {Xianchang Luo and Yinxing Xue and Zhenchang Xing and Jiamou Sun},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3560417}
}