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GitHub reported that developers accepted around 30% of GitHub Copilot suggestions in an adoption analysis at Accenture. That is a result from one company’s study—not a current acceptance rate for every Copilot user. The figure also measures suggestions accepted, not whether the code was correct or how much productivity improved.
What does the 30% figure mean?
GitHub’s Accenture analysis reported that developers accepted around 30% of Copilot suggestions. In its documentation, GitHub defines code completion acceptance rate as the percentage of suggestions users accept. Its usage dashboards show inline suggestions offered, suggestions accepted, and the resulting rate. The documented dashboards cover enterprise and organization usage; their charts do not include Copilot CLI usage. GitHub’s Copilot metrics documentation
GitHub’s account of the Accenture analysis also gives three other figures: 90% of developers reported committing code suggested by Copilot; 91% reported that their teams merged pull requests containing Copilot-suggested code; and 88% of Copilot-generated characters were retained in the editor. These describe reported downstream outcomes or retained characters, not the acceptance-rate calculation. GitHub’s Accenture adoption analysis
Does accepting a suggestion mean the code is correct?
No. Acceptance records a user action, not a verified code-quality result. GitHub researcher Albert Ziegler describes the rate as capturing how many suggestions are “deemed promising enough to accept.” A developer may accept a suggestion and later revise it; a suggestion that is not accepted may still have been useful as a starting point. Acceptance can indicate that a suggestion seemed useful, but it does not by itself establish correctness or measure productivity. GitHub’s explanation of Copilot acceptance rate
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Why did a UK trial report a different rate?
A UK public-sector AI coding assistant trial reported a 15.8% average acceptance rate for suggested code lines for GitHub Copilot. The report says telemetry was missing for the pilot’s second month. UK Government trial report
This does not directly contradict the Accenture result. The studies involved different populations and settings, and the UK report describes acceptance of suggested code lines, while GitHub’s metric is framed as the share of suggestions accepted. Period, product and editor scope, acceptance definition, and telemetry coverage can all affect a comparison. The available figures do not establish one current rate representative of all Copilot users, plans, programming languages, or IDEs.
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How should you interpret Copilot acceptance rates?
When comparing rates, check what each number actually counts and the conditions under which it was measured:
- Population: Identify the organization or group of developers studied.
- Period and product scope: Check when the data was collected and which Copilot surfaces or editors were included.
- Denominator and unit: Distinguish suggestions from suggested code lines.
- Acceptance definition: Determine what action counts as accepting a suggestion.
- Telemetry coverage: Look for missing data or other disclosed measurement limits.
Without those details, two acceptance percentages may not be meaningfully comparable. The Accenture figure is best read as a reported result from that company’s adoption analysis, rather than a universal benchmark.
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