Table of contents

Parts are whole numbers; topics are decimals (topic N.0 introduces each part). See also the Introduction.

Part 1: Foundations of Measurement

• 1.0 Introduction
• 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

Part 2: Flow Metrics

• 2.0 Introduction
• 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

Part 3: Developer Experience and the SPACE Framework

• 3.0 Introduction
• 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

Part 4: Code and Quality Metrics

• 4.0 Introduction
• 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

Part 5: Product and Business Metrics

• 5.0 Introduction
• 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

Part 6: Reliability, Operations, and Security Metrics

• 6.0 Introduction
• 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

Part 7: Metrics in the Age of AI

• 7.0 Introduction
• 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

Part 8: Building a Metrics Program

• 8.0 Introduction
• 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

Part 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