Dear Diary: A Randomized Controlled Trial of Generative AI Coding Tools in the Workplace
Open accessAbstract
Generative AI coding tools are relatively new and their impact on developers extends beyond traditional coding metrics, influencing beliefs about work and developers' roles in the workplace. This study aims to illuminate developers' preexisting beliefs about generative AI tools, their self-perceptions, and how regular use of these tools may alter these beliefs. Using a mixed methods approach, including surveys, a randomized controlled trial, and a three-week diary study, we explored the real-world application of generative AI tools within a large multinational software company. We found that the introduction and sustained use of generative AI coding tools significantly increases developers' perceptions of these tools as both useful and enjoyable. However, developers' views on the trustworthiness of AI-generated code remained unchanged. We also discovered unexpected uses of these tools, such as replacing web search and fostering creative ideation. Additionally, 84% of participants reported positive changes in their daily work practices, and 66% noted shifts in their feelings about their work, ranging from increased enthusiasm to heightened awareness of the need to stay current with technology. This research provides both qualitative and quantitative insights into the evolving role of generative AI in software development and offers practical recommendations for maximizing the benefits of this emerging technology—particularly in balancing the productivity gains from AI-generated code with the need for increased scrutiny and critical evaluation of its outputs.
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Butler et al. (2025) found that 84% reported positive changes in daily work after introducing GitHub Copilot.
“84% reported positive changes in their daily work, e.g., reduced web searching and less manual boilerplate coding.”
From Echoes of AI: Investigating the downstream effects of AI assistants on software maintainability (2026) · cites this paper as Butler et al. (2025)
Butler et al. (2025) found no statistically significant differences in telemetry data, but medium to large effect sizes for most metrics.
“A difference-in-differences analysis of telemetry data found no statistically significant differ -ences, but medium to large effect sizes for most of the metrics.”
From Echoes of AI: Investigating the downstream effects of AI assistants on software maintainability (2026) · cites this paper as Butler et al. (2025)
Butler et al. (2025) found that 66% described feeling more positive about work after introducing GitHub Copilot.
“Moreover, 66% described feeling more positive about work after introducing GitHub Copilot.”
From Echoes of AI: Investigating the downstream effects of AI assistants on software maintainability (2026) · cites this paper as Butler et al. (2025)
Liang et al. (2024) and Butler et al. (2025) found that developers generally feel productivity gains using the same assistant.
“Similarly, Liang et al. ( 2024) and Butler et al. (2025) found that developers generally feel productivity gains using the same assistant.”
From Echoes of AI: Investigating the downstream effects of AI assistants on software maintainability (2026) · cites this paper as Butler et al. (2025)
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References 21 · Cited by 12
- Authors
- Butler, J., Suh, J., Haniyur, S., & Hadley, C.
- Affiliations
- Microsoft (United States) (US)
- Institute for Life at Work
- Venue
- 2025 IEEE/ACM 47th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)
- Publisher
- IEEE
- Published
- 2025-04-27
- Pages
- 319-329
- OpenAlex
- W4413360237
- Cited by
- 12
- References
- 21
- Citations by year
- 2026: 9 · 2025: 3
- FWCI
- 13.72
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- OpenBU (Boston University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Microsoft Research
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Computer Science Applications › Open Source Software Innovations
- Keywords
- AI-generated code quality, creative idea generation
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