CodePori：利用多智能体技术实现大规模自主软件开发的系统
CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology
AI总结：
本文研究了基于大语言模型的多智能体系统在自主软件开发中的潜力与局限，通过开发CodePori系统和参与式评估，揭示了LLM多智能体系统的优势、挑战及改进方向。
摘要：
背景：基于大语言模型的多智能体系统在软件开发中实现了自动化和决策支持，但现有研究依赖于仅提供二元通过或失败结果的基准数据集，限制了对实际应用的深入理解。目标：本研究通过实证方法探讨了基于大语言模型的智能体在自主软件开发任务中的潜力和限制。方法：采用两阶段方法：首先开发用于自动化代码生成的多智能体系统CodePori，然后通过参与式评估来评估其实际性能。结果：参与者反馈揭示了基于大语言模型的多智能体系统的关键优势、挑战和改进领域，突显了标准代码生成基准所忽略的方面。结论：尽管基于大语言模型的多智能体系统在大规模软件开发中显示出潜力，但成功整合需要解决诸如内存限制、幻觉和代码异味等挑战，同时需要以实践者为中心的视角。
英文摘要：
Existing Large Language Model (LLM)-based multi-agent systems are capable of executing tasks and providing data-driven recommendations, thereby enabling automation and decision support that assist practitioners in software development. However, existing studies have evaluated agents' performance on benchmark datasets, offering only binary pass-or-fail results, which provide limited insight into their practical applicability. There remains a lack of empirical research examining the potential and limitations of LLM-based agents in addressing challenging, real-world tasks, such as automated code generation for software systems. To this end, this study conducted a survey to empirically investigate the potential of LLM-based agents in software development, with participants evaluating the agents' performance in autonomous software development tasks. We employed a two-phase approach comprising (i) the development of a multi-agent system, CodePori, to automate code generation, and (ii) a survey-based evaluation to assess agent performance and explore its practical applicability to software development. Our results highlight that, while LLM-based multi-agent systems show potential for autonomous code generation, their successful integration requires addressing specific challenges (e.g., short-term memory limitations, hallucinations, and code smells) and incorporating a practitioner-centric perspective. The study also highlights the need to move beyond standard benchmarks for evaluating real-world applicability and identifies new opportunities for broader adoption in both industry and academia.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
