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Big Data Analytics Has Little to Do with Analytics Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Predicting Issues for Resolving in the Next Release

Authors : Shien Wee Ng, Hoa Khanh Dam, Morakot Choetkiertikul, Aditya Ghose

Published in: Service Research and Innovation

Publisher: Springer International Publishing

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Abstract

Deciding which features or requirements (or commonly referred to as issues) to be implemented for the next release is an important and integral part of any type of incremental development. Existing approaches consider the next release problem as a single or multi-objective optimization problem (on customer values and implementation costs) and thus adopt evolutionary search-based techniques to address it. In this paper, we propose a novel approach to the next release problem by mining historical releases to build a predictive model for recommending if a requirement should be implemented for the next release. Results from our experiments performed on a dataset of 22,400 issues in five large open source projects demonstrate the effectiveness of our approach.

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Footnotes
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Metadata
Title
Predicting Issues for Resolving in the Next Release
Authors
Shien Wee Ng
Hoa Khanh Dam
Morakot Choetkiertikul
Aditya Ghose
Copyright Year
2018
Book
Service Research and Innovation

Print ISBN: 978-3-319-76586-0

Electronic ISBN: 978-3-319-76587-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-76587-7_11

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