用于估计故事点的深度学习模型
A deep learning model for estimating story points
摘要：
尽管在传统软件项目中，软件分析对工作量估计进行了大量研究，但在敏捷项目中，对估计的工作很少，特别是估计用户故事或问题。故事点是用于估计实现用户故事或解决问题所涉及的工作量的最常见的度量单位。在本文中，我们为\emph{first}时间提供了一个基于故事点的估计的综合数据集，其中包含来自16个开源项目的23，313个问题。我们还提出了一个预测模型，用于估计故事点，该模型基于两个强大的深度学习架构的新颖组合：长短期记忆和循环高速公路网络。我们的预测系统是\emph{end-to-end}从原始输入数据到预测结果的可训练的，而无需任何手动特征工程。实证评估表明，我们的方法始终优于三个常见的努力估计基线和两个替代品的平均绝对误差和标准化精度。
英文摘要：
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 \emph{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 \emph{end-to-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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
