Detecting and Explaining Self-Admitted Technical Debts with Attention-based Neural Networks Wang, X, Liu, J, Li, L, Chen, X, Liu, Xiao and Wu, H 2020, Detecting and Explaining Self-Admitted Technical Debts with Attention-based Neural Networks, in ASE 2020 : Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, IEEE, Piscataway, N.J., pp. 871-882, doi: 10.1145/3324884.3416583. Attached Files Name Description MIMEType Size Downloads Title Detecting and Explaining Self-Admitted Technical Debts with Attention-based Neural Networks Author(s) Wang, X Liu, J Li, L Chen, X Liu, Xiao orcid.org/0000-0001-8400-5754 Wu, H Conference name Automated Software Engineering. Conference (2020 : 35th : Online from Melbourne, Victoria) Conference location Online from Melbourne, Victoria Conference dates 21-25 Sep. 2020 Title of proceedings ASE 2020 : Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering Publication date 2020 Start page 871 End page 882 Total pages 12 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) Self-Admitted Technical Debt Word Embedding Attention-based Neural Networks SATL CORE2020 A ISBN 9781450367684 Language eng DOI 10.1145/3324884.3416583 Indigenous content off HERDC Research category E1 Full written paper - refereed Persistent URL http://hdl.handle.net/10536/DRO/DU:30147443 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 21 Jan 2021, 13:21:41 EST Fri, 22 Jan 2021, 04:02:08 EST Fri, 22 Jan 2021, 05:06:01 EST Wed, 27 Jan 2021, 11:14:29 EST Wed, 27 Jan 2021, 11:15:57 EST Wed, 27 Jan 2021, 15:15:01 EST Fri, 05 Feb 2021, 04:36:47 EST Mon, 22 Mar 2021, 01:36:47 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 49 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Thu, 21 Jan 2021, 13:21:41 EST

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