This paper presents an evaluation of the code representation model Code2vec when trained on the task of detecting security vulnerabilities in C source code. We leverage the open-source library astminer to extract path-contexts from the abstract syntax trees of a corpus of labeled C functions. Code2vec is trained on the resulting path-contexts with the task of classifying a function as vulnerable or non-vulnerable. Using the CodeXGLUE benchmark, we show that the accuracy of Code2vec for this task is comparable to simple transformer-based methods such as pre-trained RoBERTa, and outperforms more naive NLP-based methods. We achieved an accuracy of 61.43% while maintaining low computational requirements relative to larger models.
@article{arxiv.2106.01367,
title = {On using distributed representations of source code for the detection of C security vulnerabilities},
author = {David Coimbra and Sofia Reis and Rui Abreu and Corina Păsăreanu and Hakan Erdogmus},
journal= {arXiv preprint arXiv:2106.01367},
year = {2021}
}