Experience with GitHub Copilot for Developer Productivity at Zoominfo
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866929684591149056 |
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| author | Bakal, Gal Dasdan, Ali Katz, Yaniv Kaufman, Michael Levin, Guy |
| author_facet | Bakal, Gal Dasdan, Ali Katz, Yaniv Kaufman, Michael Levin, Guy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13282 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Experience with GitHub Copilot for Developer Productivity at Zoominfo Bakal, Gal Dasdan, Ali Katz, Yaniv Kaufman, Michael Levin, Guy Software Engineering Artificial Intelligence I.2.2; I.2.5; D.2.3 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. |
| title | Experience with GitHub Copilot for Developer Productivity at Zoominfo |
| topic | Software Engineering Artificial Intelligence I.2.2; I.2.5; D.2.3 |
| url | https://arxiv.org/abs/2501.13282 |