Dear Diary: A randomized controlled trial of Generative AI coding tools in the workplace
Not on the map. Why?
Abstract (arXiv)
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. Our findings reveal 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 searches and fostering creative ideation. Additionally, 84 percent of participants reported positive changes in their daily work practices, and 66 percent noted shifts in their feelings about their work, ranging from increased enthusiasm to heightened awareness of the need to stay current with technological advances. 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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Oct 2024.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2025: 2; 2026: 5.
7 papers.
- 2025Generative AI and Empirical Software Engineering: A Paradigm Shiftcited by 9
- 2026From Future of Work to Future of Workers: Addressing Asymptomatic AI Harms for Dignified Human-AI Interactioncited by 6
- 2025WhatsCode: Large-Scale GenAI Deployment for Developer Efficiency at WhatsAppcited by 2
- 2026Adoption and Impact of Command-Line AI Coding Agents: A Study of Microsoft's Early 2026 Rollout of Claude Code and GitHub Copilot CLIcited by 1
- 2026Beyond the Org Chart: AI and the Transformation of Invisible Workcited by 0
- 2026Characterizing the Quality Profile of AI-Generated C++ in Productioncited by 0
- 2026Expectations and Practices around AI Disclosure in CS Researchcited by 0
- 2026Expectations and Practices around AI Disclosure in CS Research24 Aug 2026
- 2026Characterizing the Quality Profile of AI-Generated C++ in Production6 Aug 2026
- 2026Adoption and Impact of Command-Line AI Coding Agents: A Study of Microsoft's Early 2026 Rollout of Claude Code and GitHub Copilot CLI1 Jul 2026
- 2026Beyond the Org Chart: AI and the Transformation of Invisible Work21 May 2026
- 2026From Future of Work to Future of Workers: Addressing Asymptomatic AI Harms for Dignified Human-AI Interaction29 Jan 2026
- 2025WhatsCode: Large-Scale GenAI Deployment for Developer Efficiency at WhatsApp4 Dec 2025
- 2025Generative AI and Empirical Software Engineering: A Paradigm Shift12 Feb 2025