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 user stories or issues. Story points are the
most common unit of measure used for estimating the effort
involved in implementing a user story or resolving an issue. In
this paper, we offer for the first time a comprehensive dataset for
story points-based estimation that contains 23,313 issues from 16
open source projects. We also 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 endto-
end trainable from raw input data to prediction outcomes
without any manual feature engineering. An empirical evaluation
demonstrates that our approach consistently outperforms three
common effort estimation baselines and two alternatives in both
Mean Absolute Error and the Standardized Accuracy.
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