AI 中文总结
本研究通过采用ADEMM方法对27名外部开发者开展混合方法纵向研究，发现组织依赖等效率瓶颈稳定存在，还识别出固定工具会遗漏的生成式AI使用障碍，为组织干预提供了依据。
AI 中文摘要
背景：开发者效率受技术、组织和个人因素驱动，但很少有纵向研究探索这些因素如何随时间演变。目标：本研究调查咨询与专业发展场景中阻碍开发者感知效率的主要因素，分析这些因素在重复数据收集周期中的变化情况及定性描述。方法：我们开展混合方法纵向案例研究，采用适应性开发者效率监测方法（ADEMM）对27名外部软件开发者进行研究，结合12轮定期调查与18次半结构化访谈，通过统计分析和主题分析对数据进行处理。结果：最常见的瓶颈是组织依赖和等待外部验证，二者在结构上保持稳定；其次是技术知识缺口，随开发者适应而减少。研究进行到第9轮时，定性层面出现生成式AI使用障碍，该因素被纳入调查工具，且成为访谈中编码频率最高的主题。访谈结果证实了定量发现，其中需求文档不足、组织依赖及AI相关挑战是最常出现的主题。结论：开发者感知效率具有高度动态性，无法通过单一横截面测量准确捕捉；通过ADEMM开展的适应性监测识别出固定工具会遗漏的新兴因素——生成式AI使用障碍，并在研究过程中为具体组织干预提供了依据。对于管理外部开发者的组织，应针对外部依赖、沟通渠道及开发者不断演变的AI工具使用情况采取行动。
英文摘要
Context: Developer efficiency is driven by technical, organizational, and personal factors, yet few longitudinal studies explore how these factors evolve over time. Objective: This study investigates the primary factors hindering the perceived efficiency of developers in a consulting and professional development context, analyzing how these factors vary across recurring data collection cycles and how they are described qualitatively. Method: We conducted a mixed-methods longitudinal case study applying the Adaptive Developer Efficiency Monitoring Method (ADEMM) to 27 external software developers, combining twelve waves of periodic surveys with eighteen semi-structured interviews, analyzed through statistical and thematic analysis. Results: The most frequent bottlenecks were organizational dependencies and waiting for external validation, which stayed structurally stable, followed by technical knowledge gaps, which declined as developers adapted. A generative AI usage barrier emerged qualitatively nine waves into the study, was incorporated into the survey instrument, and became the most frequently coded interview theme. Interviews corroborated the quantitative findings, with insufficient requirements documentation and organizational dependencies as the most recurrent themes alongside AI-related challenges. Conclusions: Perceived developer efficiency is highly dynamic and cannot be accurately captured through a single cross-sectional measurement. Adaptive monitoring via ADEMM identified an emerging factor, generative AI usage barriers, that a fixed instrument would have missed, and informed a concrete organizational intervention during the study. For organizations managing external developers, actions should target external dependencies, communication channels, and developers' evolving use of AI tools.
