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.
History
Pagination
944-947
Location
Seattle, Washington
Start date
2016-11-13
End date
2016-11-18
ISBN-13
9781450342186
Language
eng
Publication classification
E Conference publication, E1 Full written paper - refereed
Copyright notice
2016, ACM
Editor/Contributor(s)
Zimmermann T, Cleland-Huang J, Su Z
Title of proceedings
FSE 2016: Proceedings of the 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering
Event
ACM SIGSOFT Foundations of Software Engineering. International Symposium (24th : 2016 : Seattle, Washington)
Publisher
Association for Computing Machinery
Place of publication
New York, N.Y.
Series
ACM SIGSOFT International Symposium on Foundations of Software Engineering