Software bug prediction using graph neural networks and graph-based text representations

Abstract

While open-source communities keep growing at an impressive pace, the corresponding platforms where software engineers share their work, deliberate about software-related issues, point out undetected bugs and suggest potential features that could be incorporated, usually suffer from information overload. This paper proposes a hybrid approach that builds on the strengths of word embeddings techniques, graph-based representation of textual data and graph neural networks for the prediction of software bugs. Existing approaches aim to improve each of these components individually, thus neglecting structural or semantical underlying information. On the contrary, the approach presented in this paper aims to leverage both types of information by representing each text as a graph and utilizing Graph Attention Networks (GATs). The evaluation carried out in this paper demonstrates an overall improvement for a variety of metrics (i.e., accuracy, precision and recall), in comparison with a plethora of graph-based machine learning models. The experiments took place using four datasets of short-text GitHub and Jira issues, which are publicly available on the kaggle.com and zenodo.com platforms respectively.