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Estimating the software projects' efforts developed by agile methods isimportant for project managers or technical leads. It provides a summary as afirst 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 traditionalapproach for this problem. Graph Neural Network is a new approach that has beenapplied in Natural Language Processing for text classification. The advantagesof Graph Neural Network are based on the ability to learn information via graphdata structure, which has more representations such as the relationshipsbetween words compared to approaches of vectorizing sequence of words. In thispaper, we show the potential and possible challenges of Graph Neural Networktext classification in story point level estimation. By the experiments, weshow that the GNN Text Level Classification can achieve as high accuracy asabout 80 percent for story points level classification, which is comparable tothe traditional approach. We also analyze the GNN approach and point outseveral current disadvantages that the GNN approach can improve for thisproblem or other problems in software engineering.

Story Point Effort Estimation by Text Level Graph Neural Network