Large language models are redefining software engineering by implementing AI-powered techniques throughout the whole software development process, including requirement gathering, software architecture, code generation, testing, and deployment. However, it is still difficult to develop a cohesive platform that consistently produces the best outcomes across all stages. The objective of this study is to develop a unified platform that utilizes multiple artificial intelligence agents to automate the process of transforming user requirements into well-organized deliverables. These deliverables include user stories, prioritization, and UML sequence diagrams, along with the modular approach to APIs, unit tests, and end-to-end tests. Additionally, the platform will organize tasks, perform security and compliance, and suggest design patterns and improvements for non-functional requirements. We allow users to control and manage each phase according to their preferences. In addition, the platform provides security and compliance checks following European standards and proposes design optimizations. We use multiple models, such as GPT-3.5, GPT-4, and Llama3 to enable to generation of modular code as per user choice. The research also highlights the limitations and future research discussions to overall improve the software development life cycle. The source code for our uniform platform is hosted on GitHub, enabling additional experimentation and supporting both research and practical uses. \end

翻译：大型语言模型正在通过在整个软件开发过程中实施人工智能驱动的技术来重新定义软件工程，这些技术包括需求收集、软件架构、代码生成、测试和部署。然而，开发一个能在所有阶段始终如一地产生最佳结果的统一平台仍然很困难。本研究的目标是开发一个统一平台，该平台利用多个人工智能智能体，将用户需求自动转化为组织良好的交付成果。这些交付成果包括用户故事、优先级排序和UML序列图，以及API、单元测试和端到端测试的模块化方法。此外，该平台还将组织任务，执行安全性和合规性检查，并为非功能性需求提出设计模式和改进建议。我们允许用户根据自己的偏好控制和管理每个阶段。另外，该平台还提供遵循欧洲标准的安全与合规性检查，并提出设计优化建议。我们使用多种模型，例如GPT-3.5、GPT-4和Llama3，以支持根据用户选择生成模块化代码。该研究还强调了局限性以及未来的研究方向，以期全面改进软件开发生命周期。我们统一平台的源代码托管在GitHub上，便于进一步实验，并支持研究和实际应用。