Efficiency Gains
Compare accepted suggestion volume with engineering efficiency trends to understand where AI assistance is actually accelerating flow.
AI CODING ASSISTANTS
Measure the real impact of AI coding assistants
Oobeya helps engineering leaders understand whether GitHub Copilot, Claude, and Cursor are improving efficiency, code quality, cycle time, and DORA outcomes across the SDLC.
AI assistant efficiency SonarQube quality metrics Cycle time and PR flow DORA outcomes in one view
What teams can measure
A shared AI impact lens for engineering leaders
Track assistant adoption and usage patterns, then connect them to the engineering outcomes leadership already trusts.
SonarQube Signals
Track maintainability, reliability, security, code quality scores, and technical debt alongside AI adoption patterns.
Cycle Time
Connect AI coding assistant usage to lead time for changes, PR time to merge, and delivery flow improvements.
DORA Outcomes
Watch deployment frequency, change failure rate, and recovery signals with AI-assisted development in the same operating view.
Inside Oobeya
Operational AI impact, not dashboard theater
Oobeya combines AI coding assistant telemetry with engineering delivery and code quality signals so teams can evaluate programs with context.
AI Impact Overview
Measure adoption, efficiency, and delivery impact in one place
Oobeya brings AI coding assistant activity together with team-level engineering metrics so leaders can move from anecdotes to evidence.
Efficiency
92%
Cycle Time
5.2d
Change Fail Rate
4.8%
Adoption Metrics
Go from organization trends to team, user, seat, and language details
Review adoption rate, active users, engagement, accepted suggestions, seat utilization, and coding assistant patterns across teams and editors.
Active Users
184
Accepted Suggestions
49.9K
Seat Utilization
85.7%
Why it matters
Built for teams scaling AI-assisted development
Use one measurement system to guide rollout, enablement, adoption, quality management, and ROI discussions.
Drive adoption where it is low
Identify teams, languages, or business units with weak engagement and target enablement before licenses are wasted.
Validate quality, not just velocity
See whether higher AI usage is improving maintainability, lowering technical debt, and reducing bug trends rather than just increasing output.
Compare tools with one framework
Use a consistent measurement model across GitHub Copilot, Cursor, and Claude-centered workflows so leadership can compare programs fairly.
Tool Directory
AI coding assistants, measured in one category
Explore dedicated Oobeya pages for GitHub Copilot, Claude, and Cursor. Use one framework to connect adoption, efficiency, quality, cycle time, and DORA outcomes.
GitHub Copilot
Oobeya is integrated with GitHub Copilot and can track adoption, engagement, acceptance, efficiency, code quality, cycle time, and DORA outcomes.
Explore page
Cursor
Oobeya measures Cursor-assisted development with the same AI impact model used for adoption, efficiency, code quality, cycle time, and DORA outcomes.
Explore page
Claude
Oobeya measures Claude-centered engineering workflows across adoption, efficiency, SonarQube signals, cycle time, and DORA outcomes in one operating view.
Explore pageAI Coding Assistant Impact
Talk to an expert about AI coding assistant measurement
See how Oobeya connects adoption patterns to efficiency, SonarQube metrics, cycle time, and DORA performance for modern engineering organizations.
