Longitudinal panel study of 802 developers shows an enterprise AI coding mandate doubled per-capita merged pull requests to 2.09x baseline, with gains associated with AI adoption and accumulated use while review processes automated.
Developer Productivity With and Without GitHub Copilot: A Longitudinal Mixed-Methods Case Study
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abstract
This study investigates the real-world impact of the generative AI (GenAI) tool GitHub Copilot on developer activity and perceived productivity. We conducted a mixed-methods case study in NAV IT, a large public sector agile organization. We analyzed 26,317 unique non-merge commits from 703 of NAV IT's GitHub repositories over a two-year period, focusing on commit-based activity metrics from 25 Copilot users and 14 non-users. The analysis was complemented by survey responses on their roles and perceived productivity, as well as 13 interviews. Our analysis of activity metrics revealed that individuals who used Copilot were consistently more active than non-users, even prior to Copilot's introduction. We did not find any statistically significant changes in commit-based activity for Copilot users after they adopted the tool, although minor increases were observed. This suggests a discrepancy between changes in commit-based metrics and the subjective experience of productivity.
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cs.SE 6representative citing papers
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Longitudinal surveys show AI coding assistants reduce time on code writing but increase supervisory verification tasks, with stable productivity perceptions yet rising reports of worsened developer experience.
Students primarily used Copilot chat and code generation features during open-source contributions, with usage patterns varying significantly by gender, programming skill, and AI experience.
Qualitative interview study of 16 practitioners finds most companies at Levels 1-2 of agentic AI maturity and identifies a capability-deployment verification gap as the core barrier to production use.
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