You are not logged in.
Openly accessible

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, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Grundy, JohnORCID iD for 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
Connect to link resolver
 
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
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar 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.