Leveraging Transformer and Graph Neural Networks for Variable Misuse Detection

Understanding source code is a central part of finding and fixing software defects in software development. In many cases software defects caused by an incorrect usage of variables in program code. Over the years researchers have developed data-driven approaches to detect variable misuse. Most of modern existing approaches are based on the transformer architecture, trained on millions of buggy and correct code snippets to learn the task of variable detection. In this paper, we evaluate an alternative, a graph neural network (GNN) architectures, for variable misuse detection. Popular benchmark dataset, which is a collection functions written in Python programming language, is used to train the models presented in this paper. We compare the GNN models with the transformer-based model called CodeBERT.