arXiv:2502.15287cs.SEcs.AI2025-02被引 8

发现开发者理想与实际工作周差距大,影响效率与满意度。

Time Warp: The Gap Between Developers' Ideal vs Actual Workweeks in an AI-Driven Era

  • 分析484名开发者,对比理想与实际工作时间分配。
  • 差距越大,生产力与满意度越低,关键任务影响显著。
  • 提供数据支持AI自动化方向,匹配开发者真实需求。

本文基于对微软484名软件开发者的调查,研究理想工作周与实际工作周在时间分配上的差异。结果表明,两者间存在显著偏差,且偏差越大,开发者生产力与满意度越低。通过分析具体任务的影响,识别出若干显著影响满意度和生产力的关键活动。随着人工智能工具在软件工程中广泛应用,本研究进一步提出可优先自动化的目标任务,为未来基于数据的AI辅助开发提供实证支持,确保技术发展与开发者理想工作流一致。

原文摘要 · Abstract (English)

Software developers balance a variety of different tasks in a workweek, yet the allocation of time often differs from what they consider ideal. Identifying and addressing these deviations is crucial for organizations aiming to enhance the productivity and well-being of the developers. In this paper, we present the findings from a survey of 484 software developers at Microsoft, which aims to identify the key differences between how developers would like to allocate their time during an ideal workweek versus their actual workweek. Our analysis reveals significant deviations between a developer's ideal workweek and their actual workweek, with a clear correlation: as the gap between these two workweeks widens, we observe a decline in both productivity and satisfaction. By examining these deviations in specific activities, we assess their direct impact on the developers' satisfaction and productivity. Additionally, given the growing adoption of AI tools in software engineering, both in the industry and academia, we identify specific tasks and areas that could be strong candidates for automation. In this paper, we make three key contributions: 1) We quantify the impact of workweek deviations on developer productivity and satisfaction 2) We identify individual tasks that disproportionately affect satisfaction and productivity 3) We provide actual data-driven insights to guide future AI automation efforts in software engineering, aligning them with the developers' requirements and ideal workflows for maximizing their productivity and satisfaction.

开发者效率AI自动化工作流优化

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