用多个智能体自动完成软件开发全流程，像团队成员一样协作。
ALMAS: an Autonomous LLM-based Multi-Agent Software Engineering Framework
- 将大模型智能体按敏捷开发角色分工，协同完成任务
- 可端到端生成应用并添加新功能，支持与人类开发者无缝对接
- 模块化设计，适合集成到现有开发流程中
多智能体大语言模型系统在多个领域推动了大模型的应用进展，尤其在软件开发方面，已实现代码生成、测试和维护等环节的自动化。然而，软件开发涉及多个阶段，不仅限于编码。为此，本文提出ALMAS框架——一个基于大模型的自主多智能体软件工程体系，遵循软件开发生命周期（SDLC）理念，可在敏捷开发团队中端到端执行多项任务。该框架将智能体与敏捷角色对齐，支持模块化部署，可无缝融入人类开发者及其开发环境。通过已有成果及实际案例展示，验证了其能自动构建应用并添加新功能的能力。
原文摘要 · Abstract (English)
Multi-agent Large Language Model (LLM) systems have been leading the way in applied LLM research across a number of fields. One notable area is software development, where researchers have advanced the automation of code implementation, code testing, code maintenance, inter alia, using LLM agents. However, software development is a multifaceted environment that extends beyond just code. As such, a successful LLM system must factor in multiple stages of the software development life-cycle (SDLC). In this paper, we propose a vision for ALMAS, an Autonomous LLM-based Multi-Agent Software Engineering framework, which follows the above SDLC philosophy such that it may work within an agile software development team to perform several tasks end-to-end. ALMAS aligns its agents with agile roles, and can be used in a modular fashion to seamlessly integrate with human developers and their development environment. We showcase the progress towards ALMAS through our published works and a use case demonstrating the framework, where ALMAS is able to seamlessly generate an application and add a new feature.
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