Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 150-158.doi: 10.11896/jsjkx.210500065

• Intelligent Computing • Previous Articles     Next Articles

Deep Integrated Learning Software Requirement Classification Fusing Bert and Graph Convolution

KANG Yan, WU Zhi-wei, KOU Yong-qi, ZHANG Lan, XIE Si-yu, LI Hao   

  1. College of Software,Yunnan University,Kunming 650091,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:KANG Yan,born in 1972,Ph.D,asso-ciate professor.Her main research inte-rests includetransfer learning,deep learning and integrated learning.
    LI Hao,born in 1970,Ph.D,professor.His main research interests include distributed computing,grid and cloud computing.
  • Supported by:
    National Natural Science Foundation of China(61762092),Major Project of Yunnan Provincial Science and Technology Department(2019ZE001-1,202002AB080001-6) and Open Foundation of Yunnan Key Laboratory of Software Engineering(2020SE303).

Abstract: 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.

Key words: BERT, Ensemble learning, GCN, Software requirements, Text classification, Text features

CLC Number: 

  • TP181
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