Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (12): 81-88.doi: 10.11896/jsjkx.211100040

• Computer Software • Previous Articles     Next Articles

Automatic Assignment Method for Software Bug Based on Multivariate Features of Developers

DONG Xia-lei1, XIANG Zheng-long2, WU Hong-run3, WANG Ding-wen1, LI Yuan-xiang1   

  1. 1 School of Computer Science,Wuhan University,Wuhan 430072,China
    2 School of Computer and Software,Nanjing University of Information Science and Technology,Nanjing 210044,China
    3 School of Physics and Information Engineering,Minnan Normal University,Zhangzhou,Fujian 363000,China
  • Received:2021-11-03 Revised:2022-04-23 Published:2022-12-14
  • About author:DONG Xia-lei,born in 1998,postgra-duate.His main research interests include deep learning and intelligent software.WU Hong-run,born in 1989,Ph.D,associate professor,is a member of China Computer Federation.Her main research interests include complex networks and graph neural network.
  • Supported by:
    National Natural Science Foundation of China(62106092,61672391).

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Abstract: Software bug repair is a problem that cannot be ignored in the process of software life.How to efficiently assign software bugs automatically is a very important research direction.Now,the existing research methods mainly focus on the bugreport’s text content or the low-level information of the developers’ tossing network,while ignoring the high-level topology information in the tossing network.Therefore,this paper proposes a software bug automatic assignment model MFD-GCN based on the developers’ multivariate features.Model fully considers the high-level topological features in the developers’ tossing network,and uses the powerful network feature extraction capabilities of graph convolution network to fully mine the multivariate features that represent developers’ deep cooperation relationship and fixing preferences,and train the classifier together with the bug text features.The proposed method is evaluated on two large open-source software projects,i.e.,Eclipse and Mozilla.Expe-rimental results show that compared with the mainstream bug-assignment methods proposed in recent years,the MFD-GCN mo-del has achieved state-of-art results in recommending the top K developers.The accuracy rate of top-1 recommendation on the Eclipse and Mozilla project reaches 69.8% and 59.7%,respectively.

Key words: Automatic assignment, Bug report, Developers’ tossing network, Graph convolution network, Multivariate feature

CLC Number: 

  • TP311.5
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