基于文本级图神经网络的故事点工作量估计
Story Point Effort Estimation by Text Level Graph Neural Network
摘要：
评估敏捷方法开发的软件项目的工作量对于项目经理或技术负责人来说是非常重要的。它提供了一个摘要，作为完成任务需要多少小时和开发人员的第一个视图。有研究工作自动预测软件的努力，包括词频逆文档频率（TFIDF）作为这个问题的传统方法。图神经网络是一种应用于自然语言处理的文本分类新方法。图神经网络的优点是基于通过图数据结构学习信息的能力，与矢量化单词序列的方法相比，图数据结构具有更多的表示，例如单词之间的关系。在本文中，我们展示了图神经网络文本分类在故事点水平估计中的潜力和可能的挑战。实验结果表明，GNN文本级分类方法对故事点级分类的准确率可达80%左右，与传统方法相当。我们还分析了GNN方法，并指出了目前的GNN方法可以改善这个问题或其他问题在软件工程。
英文摘要：
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.
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。
