计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 150-158.doi: 10.11896/jsjkx.210500065
康雁, 吴志伟, 寇勇奇, 张兰, 谢思宇, 李浩
KANG Yan, WU Zhi-wei, KOU Yong-qi, ZHANG Lan, XIE Si-yu, LI Hao
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。
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