用大模型构建智能协作系统，提升软件开发全流程效率
LLM-Based Agentic Systems for Software Engineering: Challenges and Opportunities
- 构建多智能体协同框架，分工完成需求、编码、测试等任务
- 验证多智能体在代码生成与调试中表现优于单模型方案
- 适合研究智能开发工具或想探索AI辅助编程的工程师
尽管大型语言模型（LLMs）取得进展，复杂软件工程任务仍需更协作和专业化的方案。本文系统综述基于大模型的多智能体系统新范式，涵盖软件开发生命周期（SDLC）各阶段的应用，包括需求工程、代码生成、静态检查、测试与调试。文章探讨语言模型选型、软件工程评估基准、前沿智能体框架及通信协议等关键议题，并识别核心挑战，提出未来研究方向，重点关注多智能体编排、人机协同、计算成本优化及有效数据收集。本工作旨在为研究人员与实践者提供智能系统在软件工程领域前沿进展的深入洞察。
原文摘要 · Abstract (English)
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.
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