Knowledge-Guided Multi-Agent Framework for Application-Level Software Code Generation
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917035491983360 |
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| author | Xiong, Qian Yang, Bo Sun, Weisong Zhang, Yiran Li, Tianlin Liu, Yang Jin, Zhi |
| author_facet | Xiong, Qian Yang, Bo Sun, Weisong Zhang, Yiran Li, Tianlin Liu, Yang Jin, Zhi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19868 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Knowledge-Guided Multi-Agent Framework for Application-Level Software Code Generation Xiong, Qian Yang, Bo Sun, Weisong Zhang, Yiran Li, Tianlin Liu, Yang Jin, Zhi Software Engineering 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. |
| title | Knowledge-Guided Multi-Agent Framework for Application-Level Software Code Generation |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2510.19868 |