Engineering Benchmarks
Clear, standardized benchmarks that help engineering leaders assess performance, spot risks early, and drive continuous improvement across teams.
Engineering Benchmarks define how engineering performance is evaluated, compared, and improved within Oobeya.
They provide a shared reference for best practices, areas that need focus, and risk zones across the software development lifecycle.
This page explains:
How benchmarks work
How performance levels are interpreted
Which metrics are benchmarked, and how they are grouped.
What are Engineering Benchmarks?
Engineering Benchmarks are standardized performance thresholds applied to key engineering metrics in Oobeya.
They help organizations:
Compare teams fairly within a league
Identify improvement opportunities early
Create a shared language between engineering teams and leadership
Power gamification, insights, and AI-driven recommendations
Benchmarks are designed to highlight patterns and risks, not to enforce rigid targets.
Performance Levels
Each benchmarked metric is evaluated using three performance levels.
🏆 Elite
Best-in-class performance
Healthy, scalable, and sustainable engineering practices
Serves as a benchmark for other teams
⚠️ Needs Focus
Acceptable performance with clear improvement potential
Early signals of inefficiency or hidden risk
Often the highest ROI area for improvement
🚨 At Risk
Below expected performance levels
Indicates delivery, quality, reliability, or cost risk
Requires immediate attention
Benchmark Categories
Benchmarks are grouped into the following categories:
Project Management
Development
Code Review
Delivery (DORA)
Code Quality
Application Performance
Project Management
Actual Reaction Time
< 5 days
5–10 days
> 10 days
Cycle Time
< 3 days
3–10 days
> 10 days
Lead Time
< 7 days
7–14 days
> 14 days
Productivity
> 150%
100–150%
< 100%
Predictability
> 90%
70–90%
< 70%
Innovation Rate
> 75%
60–75%
< 60%
Development
Coding Efficiency
> 98%
90–98%
< 90%
Rework Rate
< 2%
2–10%
> 10%
Coding Days per Week
≥ 5 days
3–5 days
< 3 days
Code Review
Code Review Cycle Time
< 1 day
1–7 days
> 7 days
Time to Merge
< 3 days
3–30 days
> 30 days
Delivery (DORA)
Lead Time for Changes
< 1 day
1–30 days
> 30 days
Deployment Frequency
Daily
Monthly or more
Less than monthly
Time to Restore Service
< 1 day
1–30 days
> 30 days
Change Failure Rate
< 15%
15–30%
> 30%
Code Quality
Technical Debt (New Period)
< 3 days
3–30 days
> 30 days
Test Coverage (New Period)
> 80%
60–80%
< 60%
Security Severity Blocker (Overall)
0 issues
1–5 issues
> 5 issues
Security Severity High (Overall)
0 issues
1–5 issues
> 5 issues
Security Severity Blocker (New Period)
0 issues
1–5 issues
> 5 issues
Security Severity High (New Period)
0 issues
1–5 issues
> 5 issues
Security Score (Overall)
A
B–C
D–E
Reliability Score (Overall)
A
B–C
D–E
Maintainability Score (Overall)
A
B–C
D–E
App Performance
APDEX Score
> 0.85
0.70–0.85
< 0.70
Error Rate
< 1%
1–5%
> 5%
Avg. Response Time
< 300 ms
300–1000 ms
> 1000 ms
How to Use Engineering Benchmarks
Engineering Benchmarks are most effective when used to:
Detect risks early
Guide conversations instead of enforcing targets
Trigger insights and AI-driven recommendations.
Always interpret benchmarks in context, considering:
Team maturity
Product complexity
Organizational constraints.
Related Topics
AI Coach Insights
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