Code2Image: Intelligent Code Analysis by Computer Vision Techniques and Application to Vulnerability Prediction

TL;DR

This paper introduces a novel way to convert source code into images, enabling the use of computer vision techniques for code analysis and vulnerability prediction, demonstrating promising results on real-world datasets.

Contribution

The study presents a new method to represent source code as images that preserves semantic and syntactic properties, facilitating direct use in deep learning models.

Findings

Effective vulnerability prediction on real-world datasets

Outperforms some state-of-the-art solutions

Public implementation available

Abstract

Intelligent code analysis has received increasing attention in parallel with the remarkable advances in the field of machine learning (ML) in recent years. A major challenge in leveraging ML for this purpose is to represent source code in a useful form that ML algorithms can accept as input. In this study, we present a novel method to represent source code as image while preserving semantic and syntactic properties, which paves the way for leveraging computer vision techniques to use for code analysis. Indeed the method makes it possible to directly enter the resulting image representation of source codes into deep learning (DL) algorithms as input without requiring any further data pre-processing or feature extraction step. We demonstrate feasibility and effectiveness of our method by realizing a vulnerability prediction use case over a public dataset containing a large number of real-world source code samples with performance evaluation in comparison to the state-of-art solutions. Our implementation is publicly available.