Software effort can be measured by story point [35]. Current approaches for
automatically estimating story points focus on applying pre-trained embedding
models and deep learning for text regression to solve this problem which
required expensive embedding models. We propose HeteroSP, a tool for estimating
story points from textual input of Agile software project issues. We select
GPT2SP [12] and Deep-SE [8] as the baselines for comparison. First, from the
analysis of the story point dataset [8], we conclude that software issues are
actually a mixture of natural language sentences with quoted code snippets and
have problems related to large-size vocabulary. Second, we provide a module to
normalize the input text including words and code tokens of the software
issues. Third, we design an algorithm to convert an input software issue to a
graph with different types of nodes and edges. Fourth, we construct a
heterogeneous graph neural networks model with the support of fastText [6] for
constructing initial node embedding to learn and predict the story points of
new issues. We did the comparison over three scenarios of estimation, including
within project, cross-project within the repository, and cross-project cross
repository with our baseline approaches. We achieve the average Mean Absolute
Error (MAE) as 2.38, 2.61, and 2.63 for three scenarios. We outperform GPT2SP
in 2/3 of the scenarios while outperforming Deep-SE in the most challenging
scenario with significantly less amount of running time. We also compare our
approaches with different homogeneous graph neural network models and the
results show that the heterogeneous graph neural networks model outperforms the
homogeneous models in story point estimation. For time performance, we achieve
about 570 seconds as the time performance in both three processes: node
embedding initialization, model construction, and story point estimation.

本文提出了一个用于估计敏捷软件项目问题文本输入中的用户故事点的工具 HeteroSP，该工具通过构建异构图神经网络模型支持快速文本表示学习，最终在估计用户故事点方面优于基线模型，并且获得了较快的运行时间效率。