Knowledge-Guided Multi-Agent Framework for Application-Level Software Code Generation

• URL: http://arxiv.org/abs/2510.19868v1
• Date: Wed, 22 Oct 2025 03:10:58 GMT
• Title: Knowledge-Guided Multi-Agent Framework for Application-Level Software Code Generation
• Authors: Qian Xiong, Bo Yang, Weisong Sun, Yiran Zhang, Tianlin Li, Yang Liu, Zhi Jin,
• Abstract summary: This paper envisions a Knowledge-Guided Application-Level Code Generation framework named KGACG.<n> KGACG aims to trans- form software requirements specification and architectural design document into executable code.
• Score: 47.691865459572995
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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