Cited by 12Software Engineering ResearchSoftware Engineering Techniques and PracticesOpen Source Software Innovations

Dear Diary: A Randomized Controlled Trial of Generative AI Coding Tools in the Workplace

Open access
Butler, Jenna, Suh, Jina, Haniyur, Sankeerti, Hadley, Constance·2025·2025 IEEE/ACM 47th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)

Abstract

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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Crossref · 10.1109/icse-seip66354.2025.00034

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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
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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