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Predicting Issues for Resolving in the Next Release

Journal Article


Abstract


  • © 2018, Springer International Publishing AG. 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.

UOW Authors


  •   Ng, Shien Wee (external author)
  •   Dam, Hoa
  •   Choetkiertikul, Morakot (external author)
  •   Ghose, Aditya

Publication Date


  • 2018

Citation


  • Ng, S., Dam, H., Choetkiertikul, M. & Ghose, A. (2018). Predicting Issues for Resolving in the Next Release. Lecture Notes in Business Information Processing, 234 164-177.

Scopus Eid


  • 2-s2.0-85043590444

Number Of Pages


  • 13

Start Page


  • 164

End Page


  • 177

Volume


  • 234

Place Of Publication


  • Germany

Abstract


  • © 2018, Springer International Publishing AG. 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.

UOW Authors


  •   Ng, Shien Wee (external author)
  •   Dam, Hoa
  •   Choetkiertikul, Morakot (external author)
  •   Ghose, Aditya

Publication Date


  • 2018

Citation


  • Ng, S., Dam, H., Choetkiertikul, M. & Ghose, A. (2018). Predicting Issues for Resolving in the Next Release. Lecture Notes in Business Information Processing, 234 164-177.

Scopus Eid


  • 2-s2.0-85043590444

Number Of Pages


  • 13

Start Page


  • 164

End Page


  • 177

Volume


  • 234

Place Of Publication


  • Germany