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Predicting Issues for Resolving in the Next Release

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Service Research and Innovation (ASSRI 2015, ASSRI 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 234))

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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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Notes

  1. 1.

    https://docs.atlassian.com/jira/REST/latest/.

  2. 2.

    https://issues.apache.org/jira/browse/HADOOP-3816.

  3. 3.

    https://code.google.com/archive/p/randomforest-matlab/.

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Author information

Authors and Affiliations

  1. University of Wollongong, Wollongong, Australia

    Shien Wee Ng, Hoa Khanh Dam, Morakot Choetkiertikul & Aditya Ghose

Authors
  1. Shien Wee Ng
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  2. Hoa Khanh Dam
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  3. Morakot Choetkiertikul
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  4. Aditya Ghose
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Corresponding authors

Correspondence to Shien Wee Ng , Hoa Khanh Dam , Morakot Choetkiertikul or Aditya Ghose .

Editor information

Editors and Affiliations

  1. Macquarie University, Sydney, New South Wales, Australia

    Amin Beheshti

  2. CSIRO Research Organisation, Dutton Park, Queensland, Australia

    Mustafa Hashmi

  3. Royal Melbourne Institute of Technology, Melbourne, Victoria, Australia

    Hai Dong

  4. Macquarie University, Sydney, New South Wales, Australia

    Wei Emma Zhang

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Ng, S.W., Dam, H.K., Choetkiertikul, M., Ghose, A. (2018). Predicting Issues for Resolving in the Next Release. In: Beheshti, A., Hashmi, M., Dong, H., Zhang, W. (eds) Service Research and Innovation. ASSRI ASSRI 2015 2017. Lecture Notes in Business Information Processing, vol 234. Springer, Cham. https://doi.org/10.1007/978-3-319-76587-7_11

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76586-0

  • Online ISBN: 978-3-319-76587-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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