A Deep Learning Model for Estimating Story Points

@article{Choetkiertikul2019ADL,
  title={A Deep Learning Model for Estimating Story Points},
  author={Morakot Choetkiertikul and Hoa Khanh Dam and Truyen Tran and Trang Pham and Aditya K. Ghose and Tim Menzies},
  journal={IEEE Transactions on Software Engineering},
  year={2019},
  volume={45},
  pages={637-656}
}
  • Morakot Choetkiertikul, Hoa Khanh Dam, +3 authors Tim Menzies
  • Published 2019
  • Computer Science, Mathematics
  • IEEE Transactions on Software Engineering
  • 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. [...] Key Method Our prediction system is end-to-end trainable from raw input data to prediction outcomes without any manual feature engineering.Expand Abstract

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 135 REFERENCES

    Selecting Best Practices for Effort Estimation

    VIEW 1 EXCERPT

    Estimating Software Project Effort Using Analogies

    VIEW 2 EXCERPTS

    Distributed Representations of Sentences and Documents

    VIEW 2 EXCERPTS

    Beyond short snippets: Deep networks for video classification

    VIEW 1 EXCERPT

    Faster training of very deep networks via p-norm gates

    VIEW 6 EXCERPTS