Zhengye Li

CodeVulReason: Incentivizing reasoning for code vulnerability detection

This paper introduces CodeVulReason, a unified framework for enhancing the reasoning capabilities of large language models (LLMs) in code vulnerability detection (CVD), and proposes S-LoRA, a parameter-efficient fine-tuning method that optimizes Low-Rank Adaptation (LoRA) rank allocation through a statistically grounded stability-aware importance scoring mechanism.