DeepSoft: a vision for a deep model of software Dam, Hoa Khanh, Tran, Truyen, Grundy, John and Ghose, Aditya 2016, DeepSoft: a vision for a deep model of software, in FSE 2016: Proceedings of the 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, Association for Computing Machinery, New York, N.Y., pp. 944-947, doi: 10.1145/2950290.2983985. Attached Files Name Description MIMEType Size Downloads tran-deepsoft-postprint-2016.pdf Accepted version application/pdf 476.66KB 16 Title DeepSoft: a vision for a deep model of software Author(s) Dam, Hoa Khanh Tran, Truyen orcid.org/0000-0001-6531-8907 Grundy, John orcid.org/0000-0003-4928-7076 Ghose, Aditya Conference name ACM SIGSOFT Foundations of Software Engineering. International Symposium (24th : 2016 : Seattle, Washington) Conference location Seattle, Washington Conference dates 13- 18 Nov. 2016 Title of proceedings FSE 2016: Proceedings of the 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering Editor(s) Zimmermann, Thomas Cleland-Huang, Jane Su, Zhendong Publication date 2016 Conference series ACM SIGSOFT International Symposium on Foundations of Software Engineering Start page 944 End page 947 Total pages 4 Publisher Association for Computing Machinery Place of publication New York, N.Y. Keyword(s) software analytics mining software repositories Summary Although software analytics has experienced rapid growth as a research area, it has not yet reached its full potential for wide industrial adoption. Most of the existing work in software analytics still relies heavily on costly manual feature engineering processes, and they mainly address the traditional classification problems, as opposed to predicting future events. We present a vision for \emph{DeepSoft}, an \emph{end-to-end} generic framework for modeling software and its development process to predict future risks and recommend interventions. DeepSoft, partly inspired by human memory, is built upon the powerful deep learning-based Long Short Term Memory architecture that is capable of learning long-term temporal dependencies that occur in software evolution. Such deep learned patterns of software can be used to address a range of challenging problems such as code and task recommendation and prediction. DeepSoft provides a new approach for research into modeling of source code, risk prediction and mitigation, developer modeling, and automatically generating code patches from bug reports. ISBN 9781450342186 Language eng DOI 10.1145/2950290.2983985 Field of Research 080309 Software Engineering 080110 Simulation and Modelling Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1 Full written paper - refereed ERA Research output type E Conference publication Grant ID DP140102185 Copyright notice ©2016, ACM Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30085682 Document type: Conference Paper Collections: School of Information Technology Open Access Collection Related Links Link Description Connect to Elements publication management system Go to link with your DU access privileges   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. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Thu, 25 Aug 2016, 17:21:18 EST Fri, 26 Aug 2016, 05:01:02 EST Wed, 21 Dec 2016, 15:19:47 EST Wed, 21 Dec 2016, 15:20:28 EST Thu, 22 Dec 2016, 05:03:03 EST Tue, 17 Jan 2017, 08:39:57 EST Tue, 17 Jan 2017, 08:45:09 EST Tue, 17 Jan 2017, 09:02:39 EST Sat, 04 Feb 2017, 01:30:20 EST Fri, 10 Feb 2017, 14:10:04 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 58 Abstract Views, 19 File Downloads  -  Detailed Statistics Created: Wed, 21 Dec 2016, 15:20:28 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.