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

• URL: http://arxiv.org/abs/2502.15287v1
• Date: Fri, 21 Feb 2025 08:29:49 GMT
• Title: Time Warp: The Gap Between Developers' Ideal vs Actual Workweeks in an AI-Driven Era
• Authors: Sukrit Kumar, Drishti Goel, Thomas Zimmermann, Brian Houck, B. Ashok, Chetan Bansal,
• Abstract summary: We present the findings from a survey of 484 software developers at Microsoft.<n>Our analysis reveals significant deviations between a developer's ideal workweek and their actual workweek.<n>Given the growing adoption of AI tools in software engineering, we identify specific tasks and areas that could be strong candidates for automation.
• Score: 8.811930702380115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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