Experience with GitHub Copilot for Developer Productivity at Zoominfo
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Abstract (arXiv)
This paper presents a comprehensive evaluation of GitHub Copilot's deployment and impact on developer productivity at Zoominfo, a leading Go-To-Market (GTM) Intelligence Platform. We describe our systematic four-phase approach to evaluating and deploying GitHub Copilot across our engineering organization, involving over 400 developers. Our analysis combines both quantitative metrics, focusing on acceptance rates of suggestions given by GitHub Copilot and qualitative feedback given by developers through developer satisfaction surveys. The results show an average acceptance rate of 33% for suggestions and 20% for lines of code, with high developer satisfaction scores of 72%. We also discuss language-specific performance variations, limitations, and lessons learned from this medium-scale enterprise deployment. Our findings contribute to the growing body of knowledge about AI-assisted software development in enterprise settings.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jan 2025.
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Citing papers per year: 2025: 5; 2026: 3.
8 papers.
- 2025CentaurEval: Benchmarking Human-in-the-Loop Value in Agentic Codingcited by 11
- 2025The Fools are Certain; the Wise are Doubtful: Exploring LLM Confidence in Code Completioncited by 3
- 2026How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responsescited by 3
- 2026Investigating Autonomous Agent Contributions in the Wild: Activity Patterns and Code Change over Timecited by 3
- 2025Pre-Filtering Code Suggestions using Developer Behavioral Telemetry to Optimize LLM-Assisted Programmingcited by 2
- 2025The Role of GitHub Copilot on Software Development: A Perspective on Productivity, Security, Best Practices and Future Directionscited by 1
- 2025Semantic Source Code Segmentation using Small and Large Language Modelscited by 0
- 2026Echo: Learning from Experience Data via User-Driven Refinementcited by 0
- 2026Echo: Learning from Experience Data via User-Driven Refinement21 May 2026
- 2026Investigating Autonomous Agent Contributions in the Wild: Activity Patterns and Code Change over Time1 Apr 2026
- 2026How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses19 Feb 2026
- 2025CentaurEval: Benchmarking Human-in-the-Loop Value in Agentic Coding30 Nov 2025
- 2025Pre-Filtering Code Suggestions using Developer Behavioral Telemetry to Optimize LLM-Assisted Programming24 Nov 2025
- 2025The Fools are Certain; the Wise are Doubtful: Exploring LLM Confidence in Code Completion22 Aug 2025
- 2025Semantic Source Code Segmentation using Small and Large Language Models11 Jul 2025
- 2025The Role of GitHub Copilot on Software Development: A Perspective on Productivity, Security, Best Practices and Future Directions18 Feb 2025