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

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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Publication Details

Published
2025-04-27
DOI
https://doi.org/10.1109/icse-seip66354.2025.00034
Citations
1
Primary Topic
AI and HR Technologies
Type
article
Field-Weighted Citation Impact
2.76
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Dear Diary: A Randomized Controlled Trial of Generative AI Coding Tools in the Workplace

Clare Hadley, Jenna Butler, Jina Suh, Sankeerti Haniyur
1 citations
AI and HR Technologies
2.76
article

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

Clare Hadley, Jenna Butler, Jina Suh, Sankeerti Haniyur
article en
1 citations

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.

Science for Life Laboratory (SE), Microsoft Research (United Kingdom) (GB)
Gender equality
Openalex Percentile: Top 8%
AI and HR Technologies
2.76
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Dear Diary: A Randomized Controlled Trial of Generative AI Coding Tools in the Workplace — Clare Hadley, Jenna Butler, et al. · (2025) | TGRS Research Map | TGRS