分析2万次真实编码会话，揭示AI助手与开发者间的常见错位。
How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions
- 通过开发者反馈识别七类错位模式，覆盖理解意图到执行报告全过程。
- 90.5%错位导致额外工作量和信任损耗，91.5%需用户手动修正。
- 错位在不同开发环境表现不同，且随时间演变，持续影响多轮会话。
AI编程助手日益直接参与软件开发环境，但现有失败分析依赖基准轨迹，未能反映开发者真实体验中的错位问题。本研究基于1639个仓库中20,574次编程助手会话的观察性分析，将错位定义为可通过开发者反推显现的系统性失效，并沿形式、成因、成本、解决四维度标注每个事件。研究发现七类典型错位形式，涵盖助手读取项目、理解开发者意图、遵循规则、约束行为、实现与执行代码及进度汇报等环节。90.50%的错位造成额外努力与信任成本，而非不可逆系统损伤；然而91.49%的可见修复仍需用户主动干预。错位模式在IDE与CLI环境中存在差异，跨会话持续存在，并随时间演变：总体错位率下降，但约束违规与错误自我报告占比上升。研究结果为训练、评估与界面设计提供实证依据，助力提升编码助手与真实开发流程的一致性。
原文摘要 · Abstract (English)
AI coding agents increasingly act directly within software environments, yet existing analyses of their failures rely on benchmark trajectories that miss how developers actually experience misalignment. We present an observational study of 20,574 coding-agent sessions from 1,639 repositories across IDE and CLI workflows. We operationalize misalignment as a breakdown made visible through developer pushback, and annotate each episode along four axes: form, cause, cost, and resolution. We identify seven recurring forms, spanning how agents read projects, interpret developer intent, follow rules, bound their actions, implement and execute code, and report progress. 90.50% of episodes impose effort and trust costs rather than irreversible system damage, yet 91.49% of visible resolutions still require explicit user correction. Misalignment patterns also differ across IDE and CLI settings, persist across adjacent sessions, and shift over time: while overall rates decline, constraint violations and inaccurate self-reporting grow in share. Our findings inform the design of training, evaluation, and interfaces for keeping coding agents aligned with real developer workflows.
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