Despite recent advancements in Large Language Models (LLMs), complex Software Engineering (SE) tasks require more collaborative and specialized approaches. This concept paper systematically reviews the emerging paradigm of LLM-based multi-agent systems, examining their applications across the Software Development Life Cycle (SDLC), from requirements engineering and code generation to static code checking, testing, and debugging. We delve into a wide range of topics such as language model selection, SE evaluation benchmarks, state-of-the-art agentic frameworks and communication protocols. Furthermore, we identify key challenges and outline future research opportunities, with a focus on multi-agent orchestration, human-agent coordination, computational cost optimization, and effective data collection. This work aims to provide researchers and practitioners with valuable insights into the current forefront landscape of agentic systems within the software engineering domain.

翻译：尽管大型语言模型（LLM）近期取得了显著进展，但复杂的软件工程（SE）任务需要更具协作性和专业化的方法。本文系统性地综述了基于LLM的多智能体系统这一新兴范式，探讨了其在软件开发生命周期（SDLC）各阶段的应用，涵盖需求工程、代码生成、静态代码检查、测试与调试等环节。我们深入探讨了语言模型选择、软件工程评估基准、前沿智能体框架与通信协议等一系列主题。此外，本文识别了关键挑战并展望了未来研究方向，重点关注多智能体协同编排、人机协作机制、计算成本优化以及高效数据收集等核心问题。本研究旨在为学术界和工业界的研究者与实践者提供关于软件工程领域智能体系统前沿动态的深刻见解。