# AIBugHunter: A Practical Tool for Predicting, Classifying and Repairing Software Vulnerabilities

arXiv: https://arxiv.org/abs/2305.16615 · HTML: https://mlsanity.com/paper/2305.16615/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2023.

## Built on by (most cited first, 17 of 17)

- [Human-In-the-Loop Software Development Agents](https://mlsanity.com/paper/2411.12924/) (2024)
- [AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz Testing](https://mlsanity.com/paper/2409.10737/) (2024)
- [A Systematic Literature Review on Detecting Software Vulnerabilities with Large Language Models](https://mlsanity.com/paper/2507.22659/) (2025)
- [Just-in-Time Detection of Silent Security Patches](https://mlsanity.com/paper/2312.01241/) (2023)
- [EvalSVA: Multi-Agent Evaluators for Next-Gen Software Vulnerability Assessment](https://mlsanity.com/paper/2501.14737/) (2024)
- [A Systematic Study of Code Obfuscation Against LLM-based Vulnerability Detection](https://mlsanity.com/paper/2512.16538/) (2025)
- [Investigating Autonomous Agent Contributions in the Wild: Activity Patterns and Code Change over Time](https://mlsanity.com/paper/2604.00917/) (2026)
- [AI for DevSecOps: A Landscape and Future Opportunities](https://mlsanity.com/paper/2404.04839/) (2024)
- [Enhancing Source Code Security with LLMs: Demystifying The Challenges and Generating Reliable Repairs](https://mlsanity.com/paper/2409.00571/) (2024)
- [Code Readability in the Age of Large Language Models: An Industrial Case Study from Atlassian](https://mlsanity.com/paper/2501.11264/) (2025)
- [SCoPE: Evaluating LLMs for Software Vulnerability Detection](https://mlsanity.com/paper/2407.14372/) (2024)
- [Risks of ignoring uncertainty propagation in AI-augmented security pipelines](https://mlsanity.com/paper/2407.14540/) (2024)
- [Repairing vulnerabilities without invisible hands. A differentiated replication study on LLMs](https://mlsanity.com/paper/2507.20977/) (2025)
- [AgenticRepair: Multi-Faceted Program Context Engineering for Agentic Vulnerability Repair](https://mlsanity.com/paper/2607.29422/) (2026)
- [LLaVul: A Multimodal LLM for Interpretable Vulnerability Reasoning about Source Code](https://mlsanity.com/paper/2509.17337/) (2025)
- [MulVuln: Enhancing Pre-trained LMs with Shared and Language-Specific Knowledge for Multilingual Vulnerability Detection](https://mlsanity.com/paper/2510.04397/) (2025)
- [Software Vulnerability Management in the Era of Artificial Intelligence: An Industry Perspective](https://mlsanity.com/paper/2512.18261/) (2025)

## Abstract (arXiv)

Many ML-based approaches have been proposed to automatically detect, localize, and repair software vulnerabilities. While ML-based methods are more effective than program analysis-based vulnerability analysis tools, few have been integrated into modern IDEs, hindering practical adoption. To bridge this critical gap, we propose AIBugHunter, a novel ML-based software vulnerability analysis tool for C/C++ languages that is integrated into Visual Studio Code. AIBugHunter helps software developers to achieve real-time vulnerability detection, explanation, and repairs during programming. In particular, AIBugHunter scans through developers' source code to (1) locate vulnerabilities, (2) identify vulnerability types, (3) estimate vulnerability severity, and (4) suggest vulnerability repairs. In this article, we propose a novel multi-objective optimization (MOO)-based vulnerability classification approach and a transformer-based estimation approach to help AIBugHunter accurately identify vulnerability types and estimate severity. Our empirical experiments on a large dataset consisting of 188K+ C/C++ functions confirm that our proposed approaches are more accurate than other state-of-the-art baseline methods for vulnerability classification and estimation. Furthermore, we conduct qualitative evaluations including a survey study and a user study to obtain software practitioners' perceptions of our AIBugHunter tool and assess the impact that AIBugHunter may have on developers' productivity in security aspects. Our survey study shows that our AIBugHunter is perceived as useful where 90% of the participants consider adopting our AIBugHunter. Last but not least, our user study shows that our AIBugHunter could possibly enhance developers' productivity in combating cybersecurity issues during software development.