Estimating the software projects' efforts developed by agile methods is
important for project managers or technical leads. It provides a summary as a
first view of how many hours and developers are required to complete the tasks.
There are research works on automatic predicting the software efforts,
including Term Frequency Inverse Document Frequency (TFIDF) as the traditional
approach for this problem. Graph Neural Network is a new approach that has been
applied in Natural Language Processing for text classification. The advantages
of Graph Neural Network are based on the ability to learn information via graph
data structure, which has more representations such as the relationships
between words compared to approaches of vectorizing sequence of words. In this
paper, we show the potential and possible challenges of Graph Neural Network
text classification in story point level estimation. By the experiments, we
show that the GNN Text Level Classification can achieve as high accuracy as
about 80 percent for story points level classification, which is comparable to
the traditional approach. We also analyze the GNN approach and point out
several current disadvantages that the GNN approach can improve for this
problem or other problems in software engineering.

本研究探讨了使用图神经网络方法在故事点级别上进行文本分类的潜力与可能遇到的挑战，并通过实验表明 GNN 文本级分类可以达到约 80％的故事点级分类高精度。