Large Language Models (LLM) and Generative Pre-trained Transformers (GPT), are reshaping the field of Software Engineering (SE). They enable innovative methods for executing many software engineering tasks, including automated code generation, debugging, maintenance, etc. However, only a limited number of existing works have thoroughly explored the potential of GPT agents in SE. This vision paper inquires about the role of GPT-based agents in SE. Our vision is to leverage the capabilities of multiple GPT agents to contribute to SE tasks and to propose an initial road map for future work. We argue that multiple GPT agents can perform creative and demanding tasks far beyond coding and debugging. GPT agents can also do project planning, requirements engineering, and software design. These can be done through high-level descriptions given by the human developer. We have shown in our initial experimental analysis for simple software (e.g., Snake Game, Tic-Tac-Toe, Notepad) that multiple GPT agents can produce high-quality code and document it carefully. We argue that it shows a promise of unforeseen efficiency and will dramatically reduce lead-times. To this end, we intend to expand our efforts to understand how we can scale these autonomous capabilities further.

翻译：大型语言模型（LLM）与生成式预训练Transformer（GPT）正在重塑软件工程领域。它们为执行诸多软件工程任务（包括自动化代码生成、调试、维护等）提供了创新方法。然而，现有工作中仅有少数深入探索了GPT智能体在软件工程中的潜力。本愿景论文探讨了基于GPT的智能体在软件工程中的作用。我们的愿景是利用多个GPT智能体的能力协同完成软件工程任务，并为未来工作提出初步路线图。我们认为，多个GPT智能体能够执行远超编码和调试范畴的创造性高要求任务，还能进行项目规划、需求工程和软件设计，这些均可通过人类开发者提供的高层级描述实现。初步实验分析表明，针对简单软件（如贪吃蛇、井字棋、记事本），多个GPT智能体能生成高质量代码并进行细致文档记录。这预示着前所未有的效率潜力，将大幅缩短交付周期。为此，我们将持续探索如何进一步扩展这些自主能力。