Story Point Effort Estimation by Text Level Graph Neural Network

TL;DR

This paper explores using Graph Neural Networks for estimating story points in agile software projects, achieving comparable accuracy to traditional methods and discussing potential improvements.

Contribution

It introduces a GNN-based text classification approach for story point estimation and analyzes its advantages and challenges in software effort prediction.

Findings

GNN approach achieves about 80% accuracy in story point classification

GNN performance is comparable to traditional TFIDF-based methods

Identifies challenges and potential improvements for GNN in software engineering

Abstract

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.