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

arXiv: https://arxiv.org/abs/2105.03131 · HTML: https://mlsanity.com/paper/2105.03131/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 7 May 2021.

## Builds on (most cited first, 8 of 8)

- [Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks](https://mlsanity.com/paper/1909.03496/) (2019)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [Learning to Represent Programs with Graphs](https://mlsanity.com/paper/1711.00740/) (2017)
- [code2seq: Generating Sequences from Structured Representations of Code](https://mlsanity.com/paper/1808.01400/) (2018)
- [Automated Vulnerability Detection in Source Code Using Deep Representation Learning](https://mlsanity.com/paper/1807.04320/) (2018)
- [Learning to map source code to software vulnerability using code-as-a-graph](https://mlsanity.com/paper/2006.08614/) (2020)
- [Semantic Source Code Models Using Identifier Embeddings](https://mlsanity.com/paper/1904.06929/) (2019)
- [Awareness of Secure Coding Guidelines in the Industry -- A first data analysis](https://mlsanity.com/paper/2101.02085/) (2021)

## Built on by (most cited first, 2 of 2)

- [Code2Snapshot: Using Code Snapshots for Learning Representations of Source Code](https://mlsanity.com/paper/2111.01097/) (2021)
- [CV4Code: Sourcecode Understanding via Visual Code Representations](https://mlsanity.com/paper/2205.08585/) (2022)

## Abstract (arXiv)

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.