Cover Image

A Study on Mixup-Inspired Augmentation Methods for Software Vulnerability Detection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Daneshvar, Seyed Shayan, Tan, Da, Wang, Shaowei, Leung, Carson
Format:	Preprint
Published:	2025
Subjects:	Software Engineering Cryptography and Security Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Are Latent Vulnerabilities Hidden Gems for Software Vulnerability Prediction? An Empirical Study
by: Le, Triet H. M., et al.
Published: (2024)

MVD: A Multi-Lingual Software Vulnerability Detection Framework
by: Zhang, Boyu, et al.
Published: (2024)

Mitigating Data Imbalance for Software Vulnerability Assessment: Does Data Augmentation Help?
by: Le, Triet H. M., et al.
Published: (2024)

Efficient Software Vulnerability Detection Using Transformer-based Models
by: Shaik, Sameer, et al.
Published: (2026)

Learning-based Models for Vulnerability Detection: An Extensive Study
by: Ni, Chao, et al.
Published: (2024)

Software Vulnerability Prediction in Low-Resource Languages: An Empirical Study of CodeBERT and ChatGPT
by: Le, Triet H. M., et al.
Published: (2024)

deepSURF: Detecting Memory Safety Vulnerabilities in Rust Through Fuzzing LLM-Augmented Harnesses
by: Androutsopoulos, Georgios, et al.
Published: (2025)

A Manually-Curated Dataset of Fixes to Vulnerabilities of Open-Source Software
by: Ponta, Serena E., et al.
Published: (2019)

Automated Code-centric Software Vulnerability Assessment: How Far Are We? An Empirical Study in C/C++
by: Nguyen, Anh The, et al.
Published: (2024)

VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMs
by: Daneshvar, Seyed Shayan, et al.
Published: (2024)

Code-Centric Detection of Vulnerability-Fixing Commits: A Unified Benchmark and Empirical Study
by: Loose, Nils, et al.
Published: (2026)

Towards Predicting Multi-Vulnerability Attack Chains in Software Supply Chains from Software Bill of Materials Graphs
by: Baird, Laura, et al.
Published: (2026)

Automatic Data Labeling for Software Vulnerability Prediction Models: How Far Are We?
by: Le, Triet H. M., et al.
Published: (2024)

SCoPE: Evaluating LLMs for Software Vulnerability Detection
by: Gonçalves, José, et al.
Published: (2024)

Strategic Heterogeneous Multi-Agent Architecture for Cost-Effective Code Vulnerability Detection
by: Wang, Zhaohui Geoffrey
Published: (2026)

Closing the Gap: A User Study on the Real-world Usefulness of AI-powered Vulnerability Detection & Repair in the IDE
by: Steenhoek, Benjamin, et al.
Published: (2024)

Evaluating LLaMA 3.2 for Software Vulnerability Detection
by: Gonçalves, José, et al.
Published: (2025)

To Err is Machine: Vulnerability Detection Challenges LLM Reasoning
by: Steenhoek, Benjamin, et al.
Published: (2024)

Software Vulnerability Detection Using a Lightweight Graph Neural Network
by: Farmer, Miles, et al.
Published: (2026)

MARGIN: Margin-Aware Regularized Geometry for Imbalanced Vulnerability Detection
by: Zhang, Yuteng, et al.
Published: (2026)

From Lab to Reality: A Practical Evaluation of Deep Learning Models and LLMs for Vulnerability Detection
by: Lu, Chaomeng, et al.
Published: (2025)

On the Difficulty of Selecting Few-Shot Examples for Effective LLM-based Vulnerability Detection
by: Hannan, Md Abdul, et al.
Published: (2025)

Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models
by: Yang, Aidan Z. H., et al.
Published: (2024)

Towards Causal Deep Learning for Vulnerability Detection
by: Rahman, Md Mahbubur, et al.
Published: (2023)

Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectors
by: Cheng, Baijun, et al.
Published: (2024)

DLAP: A Deep Learning Augmented Large Language Model Prompting Framework for Software Vulnerability Detection
by: Yang, Yanjing, et al.
Published: (2024)

Game Rewards Vulnerabilities: Software Vulnerability Detection with Zero-Sum Game and Prototype Learning
by: Wen, Xin-Cheng, et al.
Published: (2024)

ANVIL: Anomaly-based Vulnerability Identification without Labelled Training Data
by: Wang, Weizhou, et al.
Published: (2024)

Revisiting Pre-trained Language Models for Vulnerability Detection
by: Li, Youpeng, et al.
Published: (2025)

LLM-based Vulnerability Discovery through the Lens of Code Metrics
by: Weissberg, Felix, et al.
Published: (2025)

Deep Learning Aided Software Vulnerability Detection: A Survey
by: Uddin, Md Nizam, et al.
Published: (2025)

SHERLOCK: A Deep Learning Approach To Detect Software Vulnerabilities
by: Jawwadh, Saadh, et al.
Published: (2025)

A Mixed-Methods Study of Open-Source Software Maintainers On Vulnerability Management and Platform Security Features
by: Ayala, Jessy, et al.
Published: (2024)

From Trace to Line: LLM Agent for Real-World OSS Vulnerability Localization
by: Xi, Haoran, et al.
Published: (2025)

Automated Mapping of Vulnerability Advisories onto their Fix Commits in Open Source Repositories
by: Hommersom, Daan, et al.
Published: (2021)

VulEval: Towards Repository-Level Evaluation of Software Vulnerability Detection
by: Wen, Xin-Cheng, et al.
Published: (2024)

Assessing the Software Security Comprehension of Large Language Models
by: Siddiq, Mohammed Latif, et al.
Published: (2025)

Floating-Point Neural Network Verification at the Software Level
by: Manino, Edoardo, et al.
Published: (2025)

AI-Based Software Vulnerability Detection: A Systematic Literature Review
by: Shimmi, Samiha, et al.
Published: (2025)

RESCUE: Retrieval Augmented Secure Code Generation
by: Shi, Jiahao, et al.
Published: (2025)