将两款商用AI工具联动，让开发效率提升24.5%。
Improving Performance of Commercially Available AI Products in a Multi-Agent Configuration
- 用需求生成工具共享上下文，增强代码助手建议能力。
- 代码建议准确率提升13.8%，开发者任务成功率提高24.5%。
- 实证展示商用AI工具协同的显著增效，适合研发团队参考。
近年来，随着大语言模型（LLMs）的快速发展，多智能体系统在实际应用中愈发成熟。与此同时，软件开发领域涌现出诸多AI驱动的新工具，显著优化了软件开发生命周期（SDLC）。学术界对多智能体系统在SDLC中的作用已有广泛研究，但多数聚焦于单智能体系统。现实中，公开可用的商业工具以多智能体方式协同并产生可测量提升的案例仍较少。本实验测试了基于Crowdbotics PRD AI（用于生成软件需求的AI工具）与GitHub Copilot（AI辅助编程工具）之间的上下文共享。通过共享业务需求信息，使GitHub Copilot的代码建议能力提升13.8%，开发者任务成功率提高24.5%，验证了商用AI系统在真实场景中协同工作可带来显著性能提升。
原文摘要 · Abstract (English)
In recent years, with the rapid advancement of large language models (LLMs), multi-agent systems have become increasingly more capable of practical application. At the same time, the software development industry has had a number of new AI-powered tools developed that improve the software development lifecycle (SDLC). Academically, much attention has been paid to the role of multi-agent systems to the SDLC. And, while single-agent systems have frequently been examined in real-world applications, we have seen comparatively few real-world examples of publicly available commercial tools working together in a multi-agent system with measurable improvements. In this experiment we test context sharing between Crowdbotics PRD AI, a tool for generating software requirements using AI, and GitHub Copilot, an AI pair-programming tool. By sharing business requirements from PRD AI, we improve the code suggestion capabilities of GitHub Copilot by 13.8% and developer task success rate by 24.5% -- demonstrating a real-world example of commercially-available AI systems working together with improved outcomes.
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