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A deep learning model for estimating story points

Journal Article


Abstract


  • Although there has been substantial research in software analytics for effort estimation in traditional software projects, little work has been done for estimation in agile projects, especially estimating the effort required for completing user stories or issues. Story points are the most common unit of measure used for estimating the effort involved in completing a user story or resolving an issue. In this paper, we propose a prediction model for estimating story points based on a novel combination of two powerful deep learning architectures: long short-term memory and recurrent highway network. Our prediction system is end-to-end trainable from raw input data to prediction outcomes without any manual feature engineering. We offer a comprehensive dataset for story points-based estimation that contains 23,313 issues from 16 open source projects. An empirical evaluation demonstrates that our approach consistently outperforms three common baselines (Random Guessing, Mean, and Median methods) and six alternatives (e.g. using Doc2Vec and Random Forests) in Mean Absolute Error, Median Absolute Error, and the Standardized Accuracy.

UOW Authors


  •   Choetkiertikul, Morakot (external author)
  •   Dam, Hoa
  •   Tran, Truyen (external author)
  •   Pham, Trang (external author)
  •   Ghose, Aditya
  •   Menzies, Tim (external author)

Publication Date


  • 2019

Citation


  • Choetkiertikul, M., Dam, H. Khanh., Tran, T., Pham, T., Ghose, A. & Menzies, T. (2019). A deep learning model for estimating story points. IEEE Transactions on Software Engineering, 45 (7), 637-656.

Scopus Eid


  • 2-s2.0-85041008989

Number Of Pages


  • 19

Start Page


  • 637

End Page


  • 656

Volume


  • 45

Issue


  • 7

Place Of Publication


  • United States

Abstract


  • Although there has been substantial research in software analytics for effort estimation in traditional software projects, little work has been done for estimation in agile projects, especially estimating the effort required for completing user stories or issues. Story points are the most common unit of measure used for estimating the effort involved in completing a user story or resolving an issue. In this paper, we propose a prediction model for estimating story points based on a novel combination of two powerful deep learning architectures: long short-term memory and recurrent highway network. Our prediction system is end-to-end trainable from raw input data to prediction outcomes without any manual feature engineering. We offer a comprehensive dataset for story points-based estimation that contains 23,313 issues from 16 open source projects. An empirical evaluation demonstrates that our approach consistently outperforms three common baselines (Random Guessing, Mean, and Median methods) and six alternatives (e.g. using Doc2Vec and Random Forests) in Mean Absolute Error, Median Absolute Error, and the Standardized Accuracy.

UOW Authors


  •   Choetkiertikul, Morakot (external author)
  •   Dam, Hoa
  •   Tran, Truyen (external author)
  •   Pham, Trang (external author)
  •   Ghose, Aditya
  •   Menzies, Tim (external author)

Publication Date


  • 2019

Citation


  • Choetkiertikul, M., Dam, H. Khanh., Tran, T., Pham, T., Ghose, A. & Menzies, T. (2019). A deep learning model for estimating story points. IEEE Transactions on Software Engineering, 45 (7), 637-656.

Scopus Eid


  • 2-s2.0-85041008989

Number Of Pages


  • 19

Start Page


  • 637

End Page


  • 656

Volume


  • 45

Issue


  • 7

Place Of Publication


  • United States