4 citations · 8 across the 6 of their papers we have counts for
6 papers
An Empirical Study of the Imbalance Issue in Software Vulnerability Detection
Yuejun Guo, Qiang Hu, Qiang Tang +1
Vulnerability detection is crucial to protect software security. Nowadays, deep learning (DL) is the most promising technique to automate this detection task, leveraging its superi…
Evaluating LLMs for One-Shot Patching of Real and Artificial Vulnerabilities
Aayush Garg, Zanis Ali Khan, Renzo Degiovanni +1
Automated vulnerability patching is crucial for software security, and recent advancements in Large Language Models (LLMs) present promising capabilities for automating this task.…
A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair
Zanis Ali Khan, Aayush Garg, Qiang Tang
Software vulnerabilities pose significant security threats, requiring effective mitigation. While Automated Program Repair (APR) has advanced in fixing general bugs, vulnerability…
Evaluating Pre-Trained Models for Multi-Language Vulnerability Patching
Zanis Ali Khan, Aayush Garg, Yuejun Guo +1
Software vulnerabilities pose critical security risks, demanding prompt and effective mitigation strategies. While advancements in Automated Program Repair (APR) have primarily tar…
Outside the Comfort Zone: Analysing LLM Capabilities in Software Vulnerability Detection
Yuejun Guo, Constantinos Patsakis, Qiang Hu +2
The significant increase in software production driven by automation and faster development lifecycles has resulted in a corresponding surge in software vulnerabilities. In paralle…
JUMBO: Fully Asynchronous BFT Consensus Made Truly Scalable
Hao Cheng, Yuan Lu, Zhenliang Lu +3
Recent progresses in asynchronous Byzantine fault-tolerant (BFT) consensus, e.g. Dumbo-NG (CCS' 22) and Tusk (EuroSys' 22), show promising performance through decoupling transactio…