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.

翻译：多智能体大型语言模型（LLM）系统已在多个应用领域的LLM研究中处于领先地位。软件工程作为一个重要领域，研究人员已利用LLM智能体在代码实现、代码测试、代码维护等方面推进了自动化进程。然而，软件开发是一个超越代码本身的多维度环境。因此，一个成功的LLM系统必须考虑软件开发生命周期（SDLC）的多个阶段。本文提出ALMAS（基于大型语言模型的自适应多智能体软件工程框架）的设计理念，该框架遵循上述SDLC原则，可在敏捷软件开发团队中执行端到端的多项任务。ALMAS将其智能体与敏捷开发角色对齐，并采用模块化设计实现与人类开发者及其开发环境的无缝集成。我们通过已发表的研究成果及一个演示该框架的应用案例，展示了ALMAS的进展——在该案例中，ALMAS能够无缝生成应用程序并添加新功能。