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Multi-objective search-based approach to estimate issue resolution time

Conference Paper


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Abstract


  • Background: Resolving issues is central to modern agile software development where a software is developed and evolved incrementally through series of issue resolutions. An issue could represent a requirement for a new functionality, a report of a software bug or a description of a project task.

    Aims: Knowing how long an issue will be resolved is thus important to different stakeholders including end-users, bug reporters, bug triagers, developers and managers. This paper aims to propose a multi-objective search-based approach to estimate the time required for resolving an issue.

    Methods: Using genetic programming (a meta-heuristic optimization method), we iteratively generate candidate estimate models and search for the optimal model in estimating issue resolution time. The search is guided simultaneously by two objectives: maximizing the accuracy of the estimation model while minimizing its complexity.

    Results: Our evaluation on 8,260 issues from five large open source projects demonstrate that our approach significantly (p < 0.001) outperforms both the baselines and state-of-the-art techniques.

    Conclusions: Evolutionary search-based approaches offer an effective alternative to build estimation models for issue resolution time. Using multiple objectives, one for measuring the accuracy and the other for the complexity, helps produce accurate and simple estimation models.

UOW Authors


  •   Al-Zubaidi, Wisam (external author)
  •   Dam, Hoa
  •   Ghose, Aditya
  •   Li, Xiaodong (external author)

Publication Date


  • 2017

Citation


  • Al-Zubaidi, W. H. A., Dam, H. K., Ghose, A. & Li, X. (2017). Multi-objective search-based approach to estimate issue resolution time. PROMISE: Proceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering (pp. 53-62). New York, United States: ACM.

Scopus Eid


  • 2-s2.0-85046710722

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=2652&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/1650

Start Page


  • 53

End Page


  • 62

Place Of Publication


  • New York, United States

Abstract


  • Background: Resolving issues is central to modern agile software development where a software is developed and evolved incrementally through series of issue resolutions. An issue could represent a requirement for a new functionality, a report of a software bug or a description of a project task.

    Aims: Knowing how long an issue will be resolved is thus important to different stakeholders including end-users, bug reporters, bug triagers, developers and managers. This paper aims to propose a multi-objective search-based approach to estimate the time required for resolving an issue.

    Methods: Using genetic programming (a meta-heuristic optimization method), we iteratively generate candidate estimate models and search for the optimal model in estimating issue resolution time. The search is guided simultaneously by two objectives: maximizing the accuracy of the estimation model while minimizing its complexity.

    Results: Our evaluation on 8,260 issues from five large open source projects demonstrate that our approach significantly (p < 0.001) outperforms both the baselines and state-of-the-art techniques.

    Conclusions: Evolutionary search-based approaches offer an effective alternative to build estimation models for issue resolution time. Using multiple objectives, one for measuring the accuracy and the other for the complexity, helps produce accurate and simple estimation models.

UOW Authors


  •   Al-Zubaidi, Wisam (external author)
  •   Dam, Hoa
  •   Ghose, Aditya
  •   Li, Xiaodong (external author)

Publication Date


  • 2017

Citation


  • Al-Zubaidi, W. H. A., Dam, H. K., Ghose, A. & Li, X. (2017). Multi-objective search-based approach to estimate issue resolution time. PROMISE: Proceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering (pp. 53-62). New York, United States: ACM.

Scopus Eid


  • 2-s2.0-85046710722

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=2652&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/1650

Start Page


  • 53

End Page


  • 62

Place Of Publication


  • New York, United States