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| 大语言模型智能体在软件系统根因分析中的应用综述 |
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康俊驰1,2, 丁博1,2, 冯大为1,2, 翟远钊1,2, 张迅晖1,2, 王怀民1,2
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1.国防科技大学 计算机学院, 湖南 长沙 410073;2.复杂关键软件环境全国重点实验室, 湖南 长沙 410073
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| 摘要: |
| 在现代软件系统, 尤其是云计算和微服务系统中, 根因分析是保障系统稳定性和高效运行的关键技术. 大语言模型由于其强大的自然语言处理和数据分析能力, 为根因分析提供了新的解决方案. 基于大语言模型的智能体在大语言模型的基础上, 为根因分析带来了更高的自动化程度和更精准的问题定位能力. 然而, 尽管已有相关研究探讨了大语言模型在根因分析中的应用, 但关于大语言模型智能体在根因分析的研究仍然处于早期阶段. 聚焦于大语言模型智能体在云计算和微服务系统根因分析的研究现状进行全面的分析和总结. 主要内容包括: (1)概述基于大语言模型的智能体框架构成和根因分析中的数据使用; (2)从信息获取、根因定位和效果评估这3个主要流程系统地分析大语言模型智能体在根因分析中的应用方式; (3)探讨大语言模型智能体技术在根因分析任务中面临的主要挑战和未来的发展方向. |
| 关键词: 根因分析 大语言模型智能体 软件系统 |
| DOI：10.13328/j.cnki.jos.007594 |
| 分类号:TP311 |
| 基金项目:并行与分布计算全国重点实验室开放基金(2024-KJWPDL-02); 国家重点实验室课题(231-HF-D04-01) |
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| Survey on Application of LLM-based Agents in Root Cause Analysis of Software Systems |
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KANG Jun-Chi1,2, DING Bo1,2, FENG Da-Wei1,2, ZHAI Yuan-Zhao1,2, ZHANG Xun-Hui1,2, WANG Huai-Min1,2
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1.College of Computer Science and Technology, National University of Defense and Technology, Changsha 410073, China;2.State Key Laboratory of Complex & Critical Software Environment, Changsha 410073, China
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| Abstract: |
| Root cause analysis plays a critical role in ensuring the stability and efficiency of modern software systems, particularly in cloud computing and microservice-based systems. Large language models (LLMs), with their powerful natural language processing and data analysis capabilities, have provided new solutions for root cause analysis. LLM-based agents have further enhanced root cause analysis capabilities, such as higher levels of automation and more precise problem localization. While existing research has explored the application of LLMs in root cause analysis, research on LLM-based agents is still at an early stage. To address this gap, this survey provides a comprehensive analysis and summary of current research on LLM-based agents for root cause analysis in cloud computing and microservices systems. The main contents include (1) an overview of the architecture of LLM-based agents and the types of data involved in root cause analysis; (2) a systematic analysis of how LLM-based agents are applied to root cause analysis through the main stages of information collection, root cause localization, and effectiveness evaluation; (3) an exploration of the main challenges and future directions of LLM-based agent technologies in root cause analysis tasks. |
| Key words: root cause analysis (RCA) LLM-based agent software system |
