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Experience with GitHub Copilot for Developer Productivity at Zoominfo

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arxiv 2501.13282 v1 pith:XXPJFTER submitted 2025-01-23 cs.SE cs.AI

Gal Bakal , Ali Dasdan , Yaniv Katz , Michael Kaufman , Guy Levin This is my paper · ORCID
classification cs.SEcs.AI
keywords copilotdevelopergithubacceptancedeploymentdevelopersenterprisegiven
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Developers' Experience with Generative AI Beyond Productivity Assessment -- Insights from an Empirical Mixed-Methods Field Study

    cs.SE 2026-07 unverdicted novelty 6.0 of 10

    Mixed-methods study shows developers prefer GenAI for repetitive tasks, benefit from single interaction modes but not combined ones, and gain awareness from study participation.

  2. Developers' Experience with Generative AI -- First Insights from an Empirical Mixed-Methods Field Study

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    Moderate single-mode Copilot use improved developer efficiency and reduced workload, while combined or excessive use diminished these benefits, and chat use improved task completion.

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    Fine-tuned encoder-only models such as CodeBERT outperform zero-shot and few-shot LLMs at semantic line-level segmentation of R code, and a new annotated R dataset, StatCodeSeg, is introduced.

  5. A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications

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