知识引导的多智能体框架在应用级软件代码生成中的应用 (Knowledge-Guided Multi-Agent Framework for Application-Level Software Code Generation)

Automated code generation driven by Large Lan- guage Models (LLMs) has enhanced development efficiency, yet generating complex application-level software code remains challenging. Multi-agent frameworks show potential, but existing methods perform inadequately in large-scale application-level software code generation, failing to ensure reasonable orga- nizational structures of project code and making it difficult to maintain the code generation process. To address this, this paper envisions a Knowledge-Guided Application-Level Code Generation framework named KGACG, which aims to trans- form software requirements specification and architectural design document into executable code through a collaborative closed- loop of the Code Organization & Planning Agent (COPA), Coding Agent (CA), and Testing Agent (TA), combined with a feedback mechanism. We demonstrate the collaborative process of the agents in KGACG in a Java Tank Battle game case study while facing challenges. KGACG is dedicated to advancing the automation of application-level software development.

翻译：大型语言模型（LLM）驱动的自动化代码生成提升了开发效率，但生成复杂的应用级软件代码仍然具有挑战性。多智能体框架展现出潜力，然而现有方法在大规模应用级软件代码生成中表现不佳，无法确保项目代码的合理组织结构，且难以维护代码生成过程。为解决此问题，本文提出一种名为KGACG的知识引导应用级代码生成框架，旨在通过代码组织与规划智能体（COPA）、编码智能体（CA）和测试智能体（TA）的协同闭环，结合反馈机制，将软件需求规格说明和架构设计文档转化为可执行代码。我们通过一个Java坦克大战游戏的案例研究，展示了KGACG中智能体的协同过程及其面临的挑战。KGACG致力于推动应用级软件开发的自动化进程。