融合Bert和图卷积的深度集成学习软件需求分类  

Deep Integrated Learning Software Requirement Classification Fusing Bert and Graph Convolution

作　　者：康雁[1] 吴志伟 寇勇奇 张兰 谢思宇 李浩[1] KANG Yan;WU Zhi-wei;KOU Yong-qi;ZHANG Lan;XIE Si-yu;LI Hao(College of Software,Yunnan University,Kunming 650091,China)

机构地区：[1]云南大学软件学院,昆明650091

出　　处：《计算机科学》2022年第S01期150-158,共9页Computer Science

基　　金：国家自然科学基金(61762092);云南省科技厅重大专项(2019ZE001-1,202002AB080001-6);云南省软件工程重点实验室开放基金(2020SE303)。

摘　　要：随着软件数量和种类的快速增长,有效地挖掘软件需求的文本特征,并对软件功能性需求的文本特征进行分类,成为软件工程领域的一大挑战。软件功能性需求分类为整个软件开发过程提供了可靠的保障,并减小了需求分析阶段潜在的风险和负面影响。但是,软件需求文本的高分散性、高噪声、数据稀疏等特点限制了软件需求分析的有效性。提出双层词汇图卷积网络模型,创新性地对软件需求文本进行图建模,建立软件需求的图神经网络,有效捕获单词的知识边以及单词与文本之间的关系;并提出深度集成学习模型,集成多个深度学习分类模型,对软件需求文本进行分类。在数据集Windows_a和数据集Windows_b的实验中,融合Bert和图卷积的深度集成学习模型的准确率分别达到96.73%和95.60%,其明显优于其他文本分类模型,充分证明融合Bert和图卷积的深度集成学习模型能有效判别软件需求文本的功能特性,提高软件需求文本分类的准确性。With the rapid growth of software quantity and types,effectively mine the textual features of software requirements and classify the textual features of software functional requirements becomes a major challenge in the field of software enginee-ring.The classification of software functional requirements provides a reliable guarantee for the whole software development process and reduces the potential risks and negative effects in the requirements analysis stage.However,the validity of software requirement analysis is limited by the high dispersion,high noise and sparse data of software requirement text.In this paper,a two-layer lexical graph convolutional network model(TVGCCN)is proposed to model the graph of software requirement text innovatively,build the graph neural network of software requirement,and effectively capture the knowledge edge of words and the relationship between words and text.A deep integrated learning model is proposed,which integrates several deep learning classification models to classify software requirement text.In experiments of data set Wiodows_A and data Wiodows_B,the accuracy of deep ensemble learning model integrating Bert and graph convolution reaches 96.73%and 95.60%respectively,which is ob-viously better than that of other text classification models.It is fully proved that the deep ensemble learning model integrating Bert and graph convolution can effectively distinguish the functional characteristics of software requirement text and improve the accuracy of software requirement text classification.

关 键 词：软件需求 文本分类 BERT GCN 文本特征 集成学习 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]