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

arXiv: https://arxiv.org/abs/2502.15287 · HTML: https://mlsanity.com/paper/2502.15287/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## Builds on (most cited first, 7 of 7)

- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Transforming Software Development with Generative AI: Empirical Insights on Collaboration and Workflow](https://mlsanity.com/paper/2405.01543/) (2024)
- [What happens when software developers are (un)happy](https://mlsanity.com/paper/1707.00432/) (2017)
- [Mind the Gap: On the Relationship Between Automatically Measured and Self-Reported Productivity](https://mlsanity.com/paper/2012.07428/) (2020)
- [Happy software developers solve problems better: psychological measurements in empirical software engineering](https://mlsanity.com/paper/1505.00922/) (2015)
- [Does Co-Development with AI Assistants Lead to More Maintainable Code? A Registered Report](https://mlsanity.com/paper/2408.10758/) (2024)
- [The Daily Life of Software Engineers during the COVID-19 Pandemic](https://mlsanity.com/paper/2101.04363/) (2021)

## Built on by (most cited first, 4 of 4)

- [Are We All Using Agents the Same Way? An Empirical Study of Core and Peripheral Developers Use of Coding Agents](https://mlsanity.com/paper/2601.20106/) (2026)
- [Liberating Logic in the Age of AI: Going Beyond Programming with Computational Thinking](https://mlsanity.com/paper/2511.17696/) (2025)
- [The SPACE of AI: Real-World Lessons on AI's Impact on Developers](https://mlsanity.com/paper/2508.00178/) (2025)
- [More Is Different: Toward a Theory of Emergence in AI-Native Software Ecosystems](https://mlsanity.com/paper/2604.19827/) (2026)

## Abstract (arXiv)

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.