Where is engineering effort turning into rework?
Read new work, refactoring, help, and churn together. A rise in churn is a reason to inspect recent changes and discuss what is driving repeated work.
Explore quality and reworkFor Engineering Leaders
See how your teams work. Know where to act.
Bring team performance, code quality, and AI contribution into one view. Investigate review delays, rework, and delivery risks before deciding what to change.
Team Performance
Contribution, rework, AI share, and team trendsTeam Performance Overview
Engineering performance at a glance
Team Member Insights
- New Work
- Refactor
- Help Others
- Churn
| Member | Impact Ratio | Coding Efficiency | AI Code Share | Work distribution | New Work | Refactor | Help Others | Churn |
|---|---|---|---|---|---|---|---|---|
 Maya PatelSeniorGo to Individual Scorecard | 2.4x | 94% | 36% | 52% | 25% | 17% | 6% | |
 Alex MorganMidGo to Individual Scorecard | 1.6x | 92% | 48% | 38% | 34% | 20% | 8% | |
 Sam TaylorMidGo to Individual Scorecard | 0.8x | 80% | 76% | 47% | 18% | 15% | 20% | |
 Jordan LeeJuniorGo to Individual Scorecard | 0.6x | 72% | 84% | 20% | 30% | 22% | 28% |
Coding Efficiency95.01%↑ 1.76% more than avg
Coding efficiency recovered to 95.01%, above the company average.
Coding EfficiencyExplore the trend
- Active symptoms
- 10
- Critical symptoms
- 2 need attention
- Symptom monitoring
- 10 active
- Most affected area
- Code Review (3)
Explore member work distribution and select a metric to follow its trend. Use the signals to guide team discussions and investigate the work behind a change. Explore Team Performance →
Weekly engineering review
Start with the change. Investigate the cause.
Team Performance connects the work people contribute with the trends leaders need to understand. Use it to focus the conversation, then investigate the underlying changes.
Read impact ratio, coding efficiency, AI share, and work distribution together. Role, experience, and work type give the numbers context.
What changes as AI contribution grows?
Compare AI code share with coding efficiency and delivery trends. Look for changes in review effort and rework before treating increased AI usage as an improvement.
Explore AI code attributionWhich blockers need attention this week?
Follow critical symptoms into unreviewed pull requests, review bottlenecks, and delivery delays. Agree on the next action with the team and check the trend again.
Investigate delivery symptomsFrom signals to decisions
Turn the review into a clear next step
Connect team-level trends with delivery, capacity, and AI investment decisions.
Follow delivery through production
Read production DORA metrics alongside review and development delays. Identify where a change spends its time and which part of the workflow needs attention.
Discuss capacity with evidence
Review how effort moves between new work, refactoring, rework, and helping others. Use that context to discuss priorities and support needs with your teams.
Evaluate AI beyond adoption
Connect AI contribution with pull request flow, quality, and cost. Track whether a workflow change is followed by better outcomes over time.
Customer perspectives
How engineering teams use Oobeya
“According to our POC evaluations around IT productivity measurement, Oobeya was the tool that best met our expectations. Its integration flexibility, Symptoms capability, and DORA metrics gave us visibility across nearly 4,000 developers and helped us clearly see inefficient points, including outsourced projects.”
Turkcell · Telecommunications
“Oobeya provides a comprehensive overview of our SDLC. We can see a wide range of metrics crucial for engineering efficiency by collecting data from different sources like version control, issue management, and deployment tools.”
Invicti · Cybersecurity
“What stands out the most with Oobeya is its depth of analysis and insights. It provides automated recommendations based on our team's activity, and the integration with tools like Azure DevOps and SonarQube makes the transition seamless.”
TEB (BNP Paribas) · Banking
Common questions
Engineering leadership questions
Need to discuss your team or technical setup? Contact us.
How does Oobeya help engineering leaders?
Connect delivery flow, quality, capacity, and AI impact to see where teams need attention and reduce manual reporting.
Which metrics can leaders review?
Team Performance brings together impact ratio, coding efficiency, AI code share, and work distribution. Review these alongside delivery trends, DORA metrics, quality findings, and active symptoms.
Can we focus on team-level improvement?
Yes. Start with team trends, then use member work distribution to understand the work behind them. Interpret individual metrics with role, seniority, task complexity, and team responsibilities.
How does Resource Allocation help planning?
See how engineering effort is distributed across teams, projects, and priorities to inform capacity and focus discussions.
How can we measure AI coding assistant impact?
Compare adoption with delivery, review, and quality trends in AI Impact. AI attribution with Blamely adds visibility into AI-generated code.
Which tools can we connect?
Connect source control, work tracking, CI/CD, and code quality tools. Check the integrations catalog for your specific stack.
Can Oobeya run in our infrastructure?
On-premise and private-cloud deployment options are available. Review the technical requirements with your infrastructure and security teams.
What should we bring to a walkthrough?
Bring your toolchain, team structure, and the delivery or capacity questions you want to answer. We can tailor the session to those priorities.
See Oobeya in practice
Bring your next engineering review into focus
Walk through Team Performance, delivery symptoms, and AI contribution with your team’s priorities in mind.