Leveraging Transformer and Graph Neural Networks for Variable Misuse Detection

Vitaly Romanov, Gcinizwe Dlamini, Aidar Valeev, Vladimir Ivanov

2023

Abstract

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.

Paper Citation

in Harvard Style

Romanov V., Dlamini G., Valeev A. and Ivanov V. (2023). Leveraging Transformer and Graph Neural Networks for Variable Misuse Detection. In Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE, ISBN 978-989-758-647-7, SciTePress, pages 727-733. DOI: 10.5220/0011997300003464

in Bibtex Style

@conference{enase23,
author={Vitaly Romanov and Gcinizwe Dlamini and Aidar Valeev and Vladimir Ivanov},
title={Leveraging Transformer and Graph Neural Networks for Variable Misuse Detection},
booktitle={Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,},
year={2023},
pages={727-733},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011997300003464},
isbn={978-989-758-647-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,
TI - Leveraging Transformer and Graph Neural Networks for Variable Misuse Detection
SN - 978-989-758-647-7
AU - Romanov V.
AU - Dlamini G.
AU - Valeev A.
AU - Ivanov V.
PY - 2023
SP - 727
EP - 733
DO - 10.5220/0011997300003464
PB - SciTePress