LLM驱动的代码生成智能体可自主完成开发全流程，提升工程实用性。
A Survey on Code Generation with LLM-based Agents
- 基于大模型构建自主执行开发任务的智能体，支持全流程管理。
- 覆盖从需求分解到调试的全生命周期，突破传统代码片段生成局限。
- 聚焦系统可靠性与工具集成，适合关注工程落地的研究者和开发者。
由大型语言模型（LLMs）驱动的代码生成智能体正在革新软件开发范式。与以往技术不同，其具备三大核心特征：1）自主性——能够独立管理从任务分解到编码、调试的完整工作流；2）任务范围扩展——能力超越代码片段生成，涵盖完整的软件开发生命周期（SDLC）；3）工程实用性增强——研究重点从算法创新转向系统可靠性、流程管理与工具集成等实际工程挑战。该领域近期发展迅猛，研究成果井喷，展现出巨大应用潜力。本文对基于LLM的代码生成智能体领域进行了系统性综述，追溯其技术演进历程，系统分类核心方法，包括单智能体与多智能体架构。此外，详述了此类智能体在全SDLC中的应用，总结主流评估基准与指标，整理代表性工具。最后，通过分析主要挑战，识别并提出若干基础性、长期性的未来研究方向。
原文摘要 · Abstract (English)
Code generation agents powered by large language models (LLMs) are revolutionizing the software development paradigm. Distinct from previous code generation techniques, code generation agents are characterized by three core features. 1) Autonomy: the ability to independently manage the entire workflow, from task decomposition to coding and debugging. 2) Expanded task scope: capabilities that extend beyond generating code snippets to encompass the full software development lifecycle (SDLC). 3) Enhancement of engineering practicality: a shift in research emphasis from algorithmic innovation toward practical engineering challenges, such as system reliability, process management, and tool integration. This domain has recently witnessed rapid development and an explosion in research, demonstrating significant application potential. This paper presents a systematic survey of the field of LLM-based code generation agents. We trace the technology's developmental trajectory from its inception and systematically categorize its core techniques, including both single-agent and multi-agent architectures. Furthermore, this survey details the applications of LLM-based agents across the full SDLC, summarizes mainstream evaluation benchmarks and metrics, and catalogs representative tools. Finally, by analyzing the primary challenges, we identify and propose several foundational, long-term research directions for the future work of the field.
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