Adoption & Engagement
Track active users, engaged users, adoption rate, suggestion volume, and acceptance behavior over time.
Measure GitHub Copilot impact with engineering context
Oobeya is integrated with GitHub Copilot and helps teams compare adoption, usage patterns, cost, quality, cycle time, and delivery outcomes across the SDLC.
Active vs engaged users Suggestion acceptance Team and seat utilization Cost + quality context
What teams can measure
Copilot metrics leadership can act on
Track Copilot in context, not in isolation. Use Oobeya to analyze whether adoption is associated with healthier delivery, quality, and workflow trends.
Efficiency Trends
Compare Copilot acceptance with engineering efficiency trends across teams and workflows.
Quality & SonarQube
Monitor maintainability, reliability, security scores, technical debt, and test coverage next to Copilot usage patterns.
Cycle Time & DORA
Connect Copilot usage to lead time for changes, PR time to merge, deployment frequency, and broader delivery outcomes.
Inside Oobeya
From Copilot telemetry to SDLC outcomes
Use Oobeya dashboards to move from top-line adoption and cost visibility to detailed team, user, quality, and delivery analysis.
GitHub Copilot Metrics
Measure Copilot usage down to team, user, seat, and language level
Review active users, engaged users, adoption rate, suggestions, accepted suggestions, acceptance rate, and utilization by editor or language.
Adoption Rate
85.7%
Accepted Suggestions
49.9K
Active Users
184
AI Impact Overview
Relate Copilot activity to business-relevant engineering outcomes
Go beyond raw usage by connecting Copilot telemetry to efficiency, bug trends, technical debt, quality scores, lead time, and delivery metrics.
Efficiency
93%
PR Merge Time
1.8d
Quality Score
A
GitHub Copilot use cases
Built for serious Copilot programs
Support rollout, enablement, governance, and ROI reviews with one operating view for engineering leaders.
See who is adopting Copilot and who is not
Pinpoint underused seats, inactive users, and low-engagement teams before Copilot investment becomes shelfware.
Validate quality while usage grows
Make sure higher acceptance is not creating hidden quality or maintainability problems by tracking SonarQube signals beside adoption.
Make ROI conversations evidence-based
Use one view to compare Copilot activity and cost with engineering efficiency, cycle time, quality, and DORA performance rather than relying on anecdotes.
GitHub Copilot FAQ
Questions teams ask about GitHub Copilot measurement
Clear answers for leaders evaluating Copilot rollout, adoption, quality impact, and how Oobeya connects AI assistant usage to engineering outcomes.
Teams can measure GitHub Copilot adoption, engagement, acceptance behavior, efficiency, SonarQube quality signals, cycle time, and broader delivery outcomes in one operating view.
AI Coding Assistant Impact
See GitHub Copilot impact in Oobeya
Walk through adoption, engagement, cost, SonarQube metrics, cycle time, and DORA signals in one demo tailored to your engineering organization.
