# A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks

arXiv: https://arxiv.org/abs/2403.10646 · HTML: https://mlsanity.com/paper/2403.10646/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 60 of its 213 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Builds on (most cited first, 30 of 60)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [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)
- [graph2vec: Learning Distributed Representations of Graphs](https://mlsanity.com/paper/1707.05005/) (2017)
- [Automated Vulnerability Detection in Source Code Using Deep Representation Learning](https://mlsanity.com/paper/1807.04320/) (2018)
- [DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection](https://mlsanity.com/paper/2304.00409/) (2023)
- [SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities](https://mlsanity.com/paper/1807.06756/) (2018)
- [Fast WordPiece Tokenization](https://mlsanity.com/paper/2012.15524/) (2020)
- [Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks](https://mlsanity.com/paper/2004.13826/) (2020)
- [Vulnerability Detection with Fine-grained Interpretations](https://mlsanity.com/paper/2106.10478/) (2021)
- [VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection](https://mlsanity.com/paper/2205.12424/) (2022)
- [Transformer-Based Language Models for Software Vulnerability Detection](https://mlsanity.com/paper/2204.03214/) (2022)
- [word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Data](https://mlsanity.com/paper/2003.12590/) (2020)
- [A Survey on Deep Learning for Software Engineering](https://mlsanity.com/paper/2011.14597/) (2020)
- [VulDeeLocator: A Deep Learning-based Fine-grained Vulnerability Detector](https://mlsanity.com/paper/2001.02350/) (2020)
- [Neural Transfer Learning for Repairing Security Vulnerabilities in C Code](https://mlsanity.com/paper/2104.08308/) (2021)
- [LineVD: Statement-level Vulnerability Detection using Graph Neural Networks](https://mlsanity.com/paper/2203.05181/) (2022)
- [Automated software vulnerability detection with machine learning](https://mlsanity.com/paper/1803.04497/) (2018)
- [Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2107.11598/) (2021)
- [Exploring Software Naturalness through Neural Language Models](https://mlsanity.com/paper/2006.12641/) (2020)
- [ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection](https://mlsanity.com/paper/2110.07317/) (2021)
- [MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models](https://mlsanity.com/paper/1612.04433/) (2016)
- [Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion](https://mlsanity.com/paper/2106.09282/) (2021)
- [VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements](https://mlsanity.com/paper/2112.10893/) (2021)
- [SPI: Automated Identification of Security Patches via Commits](https://mlsanity.com/paper/2105.14565/) (2021)
- [A Literature Study of Embeddings on Source Code](https://mlsanity.com/paper/1904.03061/) (2019)
- [Automated Software Vulnerability Assessment with Concept Drift](https://mlsanity.com/paper/2103.11316/) (2021)
- [PatchRNN: A Deep Learning-Based System for Security Patch Identification](https://mlsanity.com/paper/2108.03358/) (2021)

## Built on by (most cited first, 5 of 5)

- [Trace Gadgets: Minimizing Code Context for Machine Learning-Based Vulnerability Prediction](https://mlsanity.com/paper/2504.13676/) (2025)
- [MLCPD: A Unified Multi-Language Code Parsing Dataset with Universal AST Schema](https://mlsanity.com/paper/2510.16357/) (2025)
- [A Systematic Literature Review on the Use of Machine Learning in Software Engineering](https://mlsanity.com/paper/2406.13877/) (2024)
- [Exposing and Defending Membership Leakage in Vulnerability Prediction Models](https://mlsanity.com/paper/2512.08291/) (2025)
- [From Lab to Reality: A Practical Evaluation of Deep Learning Models and LLMs for Vulnerability Detection](https://mlsanity.com/paper/2512.10485/) (2025)

## Abstract (arXiv)

Machine learning techniques for cybersecurity-related software engineering tasks are becoming increasingly popular. The representation of source code is a key portion of the technique that can impact the way the model is able to learn the features of the source code. With an increasing number of these techniques being developed, it is valuable to see the current state of the field to better understand what exists and what is not there yet. This article presents a study of these existing machine learning based approaches and demonstrates what type of representations were used for different cybersecurity tasks and programming languages. Additionally, we study what types of models are used with different representations. We have found that graph-based representations are the most popular category of representation, and tokenizers and Abstract Syntax Trees (ASTs) are the two most popular representations overall (e.g., AST and tokenizers are the representations with the highest count of papers, whereas graph-based representations is the category with the highest count of papers). We also found that the most popular cybersecurity task is vulnerability detection, and the language that is covered by the most techniques is C. Finally, we found that sequence-based models are the most popular category of models, and Support Vector Machines are the most popular model overall.