Skip to main content
Software Engineering Metrics
GitHub
Home
Contents
Examples
Contributing
Project
Help
Links
Home
Book source on GitHub
Website source on GitHub
Report an issue
Index for AI agents (llms.txt)
DORA metrics
The SPACE framework (ACM Queue)
Goodhart's law (Wikipedia)
Search
⏎
Theme
Light
Dark
Language
English (US)
English (UK, Oxford spelling)
English (UK)
English (international)
العربية
বাংলা
Cymraeg
Cymraeg (Prydain Fawr)
Deutsch
Español
Français
हिन्दी
Bahasa Indonesia
日本語
한국어
Nederlands
Português
Русский
Svenska
اردو
中文
中文 (中国)
Text size
Largest
Larger
Large
Normal
Small
Smaller
Smallest
Share
Email
Mastodon
Copy link
1
Foundations of Measurement
1.0
Introduction to Part 1: Foundations of Measurement
1.1
Why measure software engineering
1.2
Goodhart's law and the psychology of metrics
1.3
Outcomes over output: choosing what to measure
1.4
Metrics governance and ownership
1.5
Data sources and instrumentation
1.6
Statistical literacy for engineering metrics
2
Flow Metrics
2.0
Introduction to Part 2: Flow Metrics
2.1
The Flow Framework
2.2
Flow items: features, defects, risks, and debt
2.3
Flow velocity and flow distribution
2.4
Flow time and flow load
2.5
Flow efficiency and work in process
2.6
Cycle time and its components
2.7
Queueing theory
2.8
Lean value stream metrics
2.9
Pull request and code review metrics
2.10
The DORA metrics framework
3
Developer Experience and the SPACE Framework
3.0
Introduction to Part 3: Developer Experience and the SPACE Framework
3.1
The SPACE framework
3.2
Satisfaction and well-being metrics
3.3
Performance metrics and outcome proxies
3.4
Activity metrics and their limits
3.5
Communication and collaboration metrics
3.6
Efficiency and flow: deep work and interruptions
3.7
Developer experience surveys and DevEx metrics
4
Code and Quality Metrics
4.0
Introduction to Part 4: Code and Quality Metrics
4.1
Code complexity metrics
4.2
Test coverage and test effectiveness
4.3
Code churn and hotspot analysis
4.4
Static analysis and code smell metrics
4.5
Technical debt measurement
4.6
Documentation and knowledge metrics
5
Product and Business Metrics
5.0
Introduction to Part 5: Product and Business Metrics
5.1
Escaped defect rate and quality escapes
5.2
Feature adoption and usage metrics
5.3
Customer and business outcome metrics
5.4
Cost and unit economics of engineering
5.5
Return on investment for engineering initiatives
6
Reliability, Operations, and Security Metrics
6.0
Introduction to Part 6: Reliability, Operations, and Security Metrics
6.1
Service level indicators, objectives, and error budgets
6.2
Incident metrics: detection, response, and recovery
6.3
On-call, capacity, and operational load metrics
6.4
Security and vulnerability management metrics
7
Metrics in the Age of AI
7.0
Introduction to Part 7: Metrics in the Age of AI
7.1
The generative AI paradigm shift
7.2
Measuring AI-assisted software development
7.3
Metric inflation and quality dilution risks
7.4
Outcome telemetry as the new north star
8
Building a Metrics Program
8.0
Introduction to Part 8: Building a Metrics Program
8.1
Designing an engineering metrics dashboard
8.2
Tooling landscape: build versus buy
8.3
Rolling out metrics without breeding fear
8.4
Maturity model for engineering metrics programs
8.5
An incremental adoption roadmap
9
Appendices
9.0
Appendices
9.1
Glossary
9.2
Metric definitions and formulas reference
9.3
Checklists
9.4
Templates
9.5
Maturity self-assessment
9.6
References and further reading
9.7
Index
Front matter
Start here: what the book is, who it is for, and the full contents.
Introduction
Table of contents
What are software engineering metrics?