AI-powered coding assistants are rapidly becoming fixtures in professional IDEs, yet their sustained influence on everyday development remains poorly understood. Prior research has focused on short-term use or self-reported perceptions, leaving open questions about how sustained AI use reshapes actual daily coding practices in the long term. We address this gap with a mixed-method study of AI adoption in IDEs, combining longitudinal two-year fine-grained telemetry from 800 developers with a survey of 62 professionals. We analyze five dimensions of workflow change: productivity, code quality, code editing, code reuse, and context switching. Telemetry reveals that AI users produce substantially more code but also delete significantly more. Meanwhile, survey respondents report productivity gains and perceive minimal changes in other dimensions. Our results offer empirical insights into the silent restructuring of software workflows and provide implications for designing future AI-augmented tooling.

翻译：AI驱动的编码助手正迅速成为专业集成开发环境中的固定配置，然而其对日常开发工作的持续影响仍鲜为人知。先前研究多聚焦于短期使用或自我报告的主观认知，关于长期持续使用AI如何重塑实际日常编码实践的问题尚未得到解答。我们通过一项IDE中AI采纳的混合方法研究填补了这一空白，该研究结合了800名开发者为期两年的细粒度纵向遥测数据与62位专业人员的问卷调查。我们分析了工作流变革的五个维度：生产力、代码质量、代码编辑、代码复用和上下文切换。遥测数据显示，AI用户产出的代码量显著增加，但删除的代码量也大幅上升。与此同时，问卷调查受访者报告了生产力提升，并认为其他维度的变化微乎其微。我们的研究结果为软件工作流程的静默重构提供了实证依据，并为设计未来AI增强工具提供了启示。