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DeepSoft: a vision for a deep model of software

Version 2 2024-06-04, 11:44
Version 1 2016-08-25, 17:20
conference contribution
posted on 2024-06-04, 11:44 authored by H Dam, Truyen TranTruyen Tran, J Grundy, A Ghose
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