Table of contents¶

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

Part 1: People¶

• 1.0 Introduction
• 1.1 Software engineering values
• 1.2 Team topologies and organizational design
• 1.3 Roles, career ladders, and growth
• 1.4 Ways of working
• 1.5 Decision-making and governance
• 1.6 Decision records
• 1.7 Engineering standards and exceptions

Part 2: Software Programming¶

• 2.0 Introduction
• 2.1 Coding standards and style
• 2.2 Software design principles
• 2.3 APIs and interface design
• 2.4 Testing strategy
• 2.5 Code review and collaboration
• 2.6 Version control and source management
• 2.7 Documentation
• 2.8 Software requirements
• 2.9 Software construction
• 2.10 Software configuration management
• 2.11 Software quality
• 2.12 Software models and methods
• 2.13 Computing, mathematical, and engineering foundations
• 2.14 Project and repository structure

Part 3: Systems¶

• 3.0 Introduction
• 3.1 Architecture fundamentals
• 3.2 Architectural styles and patterns
• 3.3 Distributed systems
• 3.4 Data architecture and storage
• 3.5 Scalability, performance, and resilience
• 3.6 Legacy modernization
• 3.7 Software maintenance
• 3.8 Interoperability and open standards
• 3.9 Systems engineering
• 3.10 Embedded and real-time systems

Part 4: Security¶

• 4.0 Introduction
• 4.1 Security foundations and culture
• 4.2 Application security
• 4.3 Infrastructure and cloud security
• 4.4 Security operations
• 4.5 Privacy and data protection
• 4.6 Compliance and governance

Part 5: UI/UX Design¶

• 5.0 Introduction
• 5.1 UX foundations
• 5.2 UI design and design systems
• 5.3 Accessibility
• 5.4 Content and communication design
• 5.5 Internationalization and localization
• 5.6 Frontend engineering
• 5.7 Mobile application development

Part 6: Artificial Intelligence¶

• 6.0 Introduction
• 6.1 AI strategy and readiness
• 6.2 Machine learning engineering (MLOps)
• 6.3 Generative AI and LLM applications
• 6.4 AI-assisted software development
• 6.5 Responsible and trustworthy AI
• 6.6 AI infrastructure and operations

Part 7: Data, Analytics, and Insight¶

• 7.0 Introduction
• 7.1 Data strategy and governance
• 7.2 Data engineering
• 7.3 Analytics and business intelligence
• 7.4 Product analytics and experimentation
• 7.5 Decision science and data-informed culture

Part 8: Automation¶

• 8.0 Introduction
• 8.1 CI/CD and delivery
• 8.2 Infrastructure as code and configuration
• 8.3 Containers, orchestration, and cloud-native
• 8.4 Platform engineering and developer experience
• 8.5 Test and process automation

Part 9: Operations, Reliability, and Observability¶

• 9.0 Introduction
• 9.1 Site reliability engineering
• 9.2 Observability and telemetry
• 9.3 Incident management
• 9.4 Cost, sustainability, and green software

Part 10: Project/Product/Program Management¶

• 10.0 Introduction
• 10.1 Portfolio and program management
• 10.2 Risk, audit, and assurance
• 10.3 Procurement, open source, and licensing
• 10.4 Sustaining large and long-lived systems
• 10.5 Ethics, accountability, and public interest
• 10.6 Project management
• 10.7 Agile
• 10.8 Maturity models
• 10.9 Innovation partnership
• 10.10 Software engineering economics
• 10.11 Digital sovereignty
• 10.12 Open source vs closed source
• 10.13 Interorganization collaboration

Part 11: Flow: Discovery and Delivery Pipelines¶

• 11.0 Introduction
• 11.1 The discovery pipeline
• 11.2 The delivery pipeline
• 11.3 Queueing theory
• 11.4 Objectives, key results, and key performance indicators

Part 12: Appendices¶

• 12.0 Appendices
• 12.1 Glossary
• 12.2 Checklists
• 12.3 Templates
• 12.4 Maturity self-assessment
• 12.5 References
• 12.6 Adoption roadmap
• 12.7 Index