Exploiting gated graph neural network for detecting and explaining self-admitted technical debts

doi:10.1016/j.jss.2022.111219

DRO

Exploiting gated graph neural network for detecting and explaining self-admitted technical debts

Yu, J, Zhao, K, Liu, J, Liu, X, Xu, Z and Wang, X 2022, Exploiting gated graph neural network for detecting and explaining self-admitted technical debts, Journal of Systems and Software, vol. 187, pp. 1-12, doi: 10.1016/j.jss.2022.111219.

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Name			Description	MIMEType		Size	Downloads

Title	Exploiting gated graph neural network for detecting and explaining self-admitted technical debts
Author(s)	Yu, J Zhao, K Liu, J Liu, X Xu, Z Wang, X
Journal name	Journal of Systems and Software
Volume number	187
Article ID	111219
Start page	1
End page	12
Total pages	12
Publisher	Elsevier
Place of publication	Amsterdam, The Netherlands
Publication date	2022-05
ISSN	0164-1212
Keyword(s)	Technical debt Self-admitted technical debt Gated graph neural network Attention mechanism
Language	eng
DOI	10.1016/j.jss.2022.111219
Field of Research	0803 Computer Software 0804 Data Format 0806 Information Systems
HERDC Research category	C1 Refereed article in a scholarly journal
Persistent URL	http://hdl.handle.net/10536/DRO/DU:30161443

Document type:	Journal Article
Collections:	Faculty of Science, Engineering and Built Environment School of Information Technology


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