LLM智能体赋能软件工程，提升代码生成与问题解决能力
Large Language Model-Based Agents for Software Engineering: A Survey
- 将大模型与外部工具结合，构建可感知环境的智能体
- 覆盖124篇论文，系统梳理智能体在软件工程中的应用
- 适合关注AI辅助开发与自动化测试的研究者
大语言模型（LLM）的进展催生了基于LLM的智能体新范式。相比独立的LLM，LLM智能体通过引入对外部资源和工具的感知与调用能力，显著拓展了模型的实用性与专业性。目前，该技术已在软件工程领域展现显著成效。多智能体协同与人机交互进一步提升了应对复杂现实问题的潜力。本文系统综述了面向软件工程的LLM智能体，共收集124篇相关论文，从软件工程与智能体两个视角进行分类，并探讨该领域的开放挑战与未来方向。相关论文列表仓库见 https://github.com/FudanSELab/Agent4SE-Paper-List。
原文摘要 · Abstract (English)
The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. Compared to standalone LLMs, LLM-based agents substantially extend the versatility and expertise of LLMs by enhancing LLMs with the capabilities of perceiving and utilizing external resources and tools. To date, LLM-based agents have been applied and shown remarkable effectiveness in Software Engineering (SE). The synergy between multiple agents and human interaction brings further promise in tackling complex real-world SE problems. In this work, we present a comprehensive and systematic survey on LLM-based agents for SE. We collect 124 papers and categorize them from two perspectives, i.e., the SE and agent perspectives. In addition, we discuss open challenges and future directions in this critical domain. The repository of this survey is at https://github.com/FudanSELab/Agent4SE-Paper-List.
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