AI 中文总结
该研究基于TMS理论，通过对152名软件专业人员的调查数据建模，揭示AI采用与社区异味的关系取决于协作类型，为相关研究与实践提供了循证模型与启示。
AI 中文摘要
背景：AI辅助开发工具的日益普及正在改变软件团队的协作、知识共享与协调方式，但其对团队社会动态的影响仍在很大程度上未被探索。缺口：目前尚不清楚AI的采用是否与社区异味（反映协调与沟通故障的社会技术反模式）的增加或减少相关，以及通过何种机制产生影响。方法：基于交互记忆系统（Transactive Memory Systems, TMS）理论，我们验证了人类-AI与人类-人类交互在TMS的两个维度（专业化与协调）上的工具，并对152名使用AI工具的软件专业人员的调查数据测试了5个偏最小二乘结构方程模型（PLS-SEM）。社区异味构念源自文献，通过专家调查与因子分析完成验证。结果：AI采用与社区异味的关系并非单一，而是取决于工作类型的机制作用：在专业化工作中，AI与更高的知识共享同伴互动相关，进而与更少的异味相关；在协调工作中，AI与更高的沟通质量直接相关，起到补充而非替代人类互动的作用。贡献：我们提供了一个基于TMS、经实证验证的模型，表明AI与社区异味的关系取决于协作类型，同时提供了可复用的工具及对研究与实践的循证启示。
英文摘要
Context: The growing adoption of AI-assisted development tools is changing how software teams collaborate, share knowledge, and coordinate, yet its consequences for team social dynamics remain largely unexplored. Gap: It is unclear whether AI adoption is associated with an increase or reduction in community smells,socio-technical anti-patterns reflecting coordination and communication breakdowns,and through which mechanisms. Method: Grounded in Transactive Memory Systems (TMS) theory, we validate instruments for HumanAI and HumanHuman interaction along two TMS dimensions, Specialization and Coordination, and test five PLS-SEM models on survey data from 152 software professionals using AI tools. Community smell constructs were derived from the literature and validated through expert surveys and factor analysis. Results: AI adoption relates to community smells not in a single way, but through mechanisms depending on the work. In specialization work, AI is associated with higher knowledge-sharing peer interaction, which is in turn associated with fewer smells. In coordination work, AI is directly associated with higher communication quality, complementing rather than replacing human interaction. Contributions: We provide an empirically validated, TMS-grounded model showing that the AIcommunity-smell relationship is contingent on the type of collaboration, with a reusable instrument and evidence-based implications for research and practice.
