Experience with GitHub Copilot for Developer Productivity at Zoominfo

2025-01-23

AI Summary

This study addresses the lack of empirical evidence on the practical efficacy of AI programming assistants in mid-sized enterprises by conducting a systematic, organization-wide evaluation of GitHub Copilot’s deployment across Zoominfo’s GTM platform (400+ developers). Employing a four-phase mixed-methods approach, we integrate developer interaction logs, multilingual code adoption metrics—quantified at both the line-level and suggestion-level—and structured satisfaction surveys to deliver the first enterprise-scale, quantitative impact analysis of Copilot outside hyperscale tech firms. Results show a 33% suggestion acceptance rate, 20% line-level code adoption rate, and 72% overall developer satisfaction; performance is strongest for Python and TypeScript, while Java and Go exhibit significant adoption bottlenecks. We propose a language-aware adoption measurement model and distill an actionable, reusable enterprise AI coding assistance framework with empirically grounded implementation guidelines and best practices.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: User Experience and Usability
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• GitHub Copilot
• developer productivity
• programming experience

Innovation

• GitHub Copilot
• AI in software development
• developer productivity enhancement