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.

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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.

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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

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Benchmark Categories

Benchmarks are grouped into the following categories:

• Project Management

• Development

• Code Review

• Delivery (DORA)

• Code Quality

• Application Performance

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Project Management

Metric	Elite	Needs Focus	At Risk
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

Metric	Elite	Needs Focus	At Risk
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

Metric	Elite	Needs Focus	At Risk
Code Review Cycle Time	< 1 day	1–7 days	> 7 days
Time to Merge	< 3 days	3–30 days	> 30 days

Delivery (DORA)

Metric	Elite	Needs Focus	At Risk
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

Metric	Elite	Needs Focus	At Risk
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

Metric	Elite	Needs Focus	At Risk
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

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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.

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Related Topics

• Gamification for Engineering KPIs

• AI Coach Insights

• Engineering Metrics List

• Delivery & DORA Metrics