LLMs 对软件开发中团队协作的影响
Impact of LLMs on Team Collaboration in Software Development
AI总结：
本文研究了 LLMs 对软件开发生命周期中团队协作的影响，通过文献综述、调查和案例研究评估了 LLM 辅助工具的作用，发现其能提升效率与沟通但也带来隐私等挑战，并提出了未来研究方向。
摘要：
大型语言模型（LLMs）正日益被整合到软件开发流程中，有望改变团队工作流和生产力。本文研究了 LLMs 如何在整个软件开发生命周期（SDLC）中影响团队协作。我们以 2025 年的最新进展重新构建并更新了先前的研究，纳入了新的文献和案例研究。我们概述了 SDLC 中的协作障碍问题，并探讨了 LLMs 如何在团队背景下提升生产力、沟通和决策。通过文献综述、行业实例、团队调查和两项案例研究，我们评估了 LLM 辅助工具（如代码生成助手和 AI 驱动的项目管理智能体）对协作软件工程实践的影响。研究结果表明，LLMs 能够显著提高效率（通过自动化重复性任务和文档编写）、增强沟通清晰度并促进跨职能协作，同时也会引入模型局限性和隐私担忧等新挑战。我们讨论了这些益处与挑战，提出了指导该调查的研究问题，评估了效度威胁，并建议了未来的研究方向，包括领域特定模型定制、改进与开发工具的集成，以及确保信任和安全的稳健策略。
英文摘要：
Large Language Models (LLMs) are increasingly being integrated into software development processes, with the potential to transform team workflows and productivity. This paper investigates how LLMs affect team collaboration throughout the Software Development Life Cycle (SDLC). We reframe and update a prior study with recent developments as of 2025, incorporating new literature and case studies. We outline the problem of collaboration hurdles in SDLC and explore how LLMs can enhance productivity, communication, and decision-making in a team context. Through literature review, industry examples, a team survey, and two case studies, we assess the impact of LLM-assisted tools (such as code generation assistants and AI-powered project management agents) on collaborative software engineering practices. Our findings indicate that LLMs can significantly improve efficiency (by automating repetitive tasks and documentation), enhance communication clarity, and aid cross-functional collaboration, while also introducing new challenges like model limitations and privacy concerns. We discuss these benefits and challenges, present research questions guiding the investigation, evaluate threats to validity, and suggest future research directions including domain-specific model customization, improved integration into development tools, and robust strategies for ensuring trust and security.
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