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 an approach to
employ LLMs to act as different roles to simulate real-life code review
process, engaging in discussions towards a consensus on the existence and
classification of vulnerabilities in the code. Preliminary evaluation of the
proposed approach indicates a 4.73% increase in the precision rate, 58.9%
increase in the recall rate, and a 28.1% increase in the F1 score.

大语言模型可用于模拟软件开发生命周期中不同角色的讨论，提高漏洞检测的准确性、召回率和 F1 得分。

多角色共识下的漏洞检测

Multi-role Consensus through LLMs Discussions for Vulnerability  Detection

In the context of designing and implementing ethical Artificial Intelligence
(AI), varying perspectives exist regarding developing trustworthy AI for
autonomous cars. This study sheds light on the differences in perspectives and
provides recommendations to minimize such divergences. By exploring the diverse
viewpoints, we identify key factors contributing to the differences and propose
strategies to bridge the gaps. This study goes beyond the trolley problem to
visualize the complex challenges of trustworthy and ethical AI. Three pillars
of trustworthy AI have been defined: transparency, reliability, and safety.
This research contributes to the field of trustworthy AI for autonomous cars,
providing practical recommendations to enhance the development of AI systems
that prioritize both technological advancement and ethical principles.

这项研究探讨在设计和实施具有伦理问题的人工智能时，对于为自动驾驶汽车开发可信人工智能存在不同的观点，旨在通过探索多元观点，识别造成差异的关键因素，并提出弥合差距的策略。研究的三个支柱为透明度、可靠性和安全性，为自动驾驶汽车的可信人工智能领域做出了贡献，提供了实际建议来提高技术进步和道德原则的平衡发展。