2024/03/21 by Zhenyu Mao, Jialong Li, Mao, Zhenyu +6 · 6 citations
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Network Security and Intrusion Detection #Software Engineering (cs.SE) #Web Application Security Vulnerabilities
paper · pdf · doi:10.48550/arxiv.2403.14274
openalex publication_date 2024/03/21 · openalex created_date 2024/03/24 · openalex updated_date 2026/07/28
Recent advancements in large language models (LLMs) have highlighted the potential for vulnerability detection, a crucial component of software quality assurance. Despite this progress, most studies have been limited to the perspective of a single role, usually testers, lacking diverse viewpoints from different roles in a typical software development life-cycle, including both developers and testers. To this end, this paper introduces a multi-role approach to employ LLMs to act as different roles simulating a real-life code review process and engaging in discussions toward a consensus on the existence and classification of vulnerabilities in the code. Preliminary evaluation of this approach indicates a 13.48% increase in the precision rate, an 18.25% increase in the recall rate, and a 16.13% increase in the F1 score.