@inproceedings{5c858c33f10a406083bce2065cd5c1dd,
title = "Predicting Issues for Resolving in the Next Release",
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.",
keywords = "Next release problem, Software analytics, Software as a Service (SaaS)",
author = "Ng, \{Shien Wee\} and Dam, \{Hoa Khanh\} and Morakot Choetkiertikul and Aditya Ghose",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG.; 6th Australasian Symposium on Service Research and Innovation, ASSRI 2017 ; Conference date: 19-10-2017 Through 20-10-2017",
year = "2018",
doi = "10.1007/978-3-319-76587-7\_11",
language = "English",
isbn = "9783319765860",
series = "Lecture Notes in Business Information Processing",
publisher = "Springer Verlag",
pages = "164--177",
editor = "Amin Beheshti and Zhang, \{Wei Emma\} and Hai Dong and Mustafa Hashmi",
booktitle = "Service Research and Innovation - 5th and 6th Australasian Symposium, ASSRI 2015 and ASSRI 2017, Revised Selected Papers",
}