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Chapters

This textbook is organized into 18 chapters covering 350 concepts across all major areas of the Architecture Tradeoff Analysis Method.

Chapter Overview

  1. Software Architecture Foundations — Core vocabulary: architecture definition, components, views, decisions, quality, and technical debt.
  2. Architecture Principles and Governance — Conformance, review boards, principles, modular design, and evolutionary architecture.
  3. ATAM Introduction and Process Phases — ATAM definition, Phase 1 and Phase 2, team roles, scripted presentations, and related methods.
  4. Stakeholder and Business Analysis — Business goals, stakeholder engagement, organizational context, and architecture vision.
  5. Quality Attributes — The eight canonical quality attributes, taxonomy, conflicts, prioritization, and requirements.
  6. Quality Attribute Scenarios — The six-component stimulus-response model, scenario types, and typed scenario families.
  7. Utility Trees and Scenario Prioritization — Utility tree structure, importance/difficulty scoring, and workshop facilitation.
  8. Architectural Patterns and Styles — Microservices, event-driven, DDD, REST, gRPC, and foundational architecture patterns.
  9. Architectural Tactics and Design Principles — Quality attribute tactics, resilience patterns, coupling, cohesion, and encapsulation.
  10. Sensitivity, Tradeoffs, Risk Analysis, and ATAM Reporting — Risks, tradeoffs, sensitivity points, risk register, and ATAM evaluation reports.
  11. Distributed Systems Architecture Fundamentals — Service decomposition, gateways, messaging, transactions, CAP theorem, and consistency.
  12. Distributed Systems Patterns — Service contracts, API versioning, integration patterns, Saga, sidecar, and database patterns.
  13. Cloud-Native Architecture — Containers, Kubernetes, serverless, deployment strategies, resilience, and cloud tradeoffs.
  14. Security Architecture — Threat modeling, zero trust, authentication, encryption, SIEM, and security tactics.
  15. Performance Engineering and Scaling — Latency, throughput, load testing, scaling strategies, CDN, and observability fundamentals.
  16. Observability, Reliability, and Cloud Operations — SLO/SLI, SRE, disaster recovery, RTO/RPO, and cloud observability.
  17. AI and Machine Learning System Architecture — ML pipelines, model serving, LLM/RAG/GraphRAG, AI observability, and responsible AI.
  18. Advanced Data, Emerging AI, and Autonomous Architectures — Data mesh, lakehouse, lambda/kappa architectures, edge AI, and autonomous systems.

How to Use This Textbook

Chapters are ordered to respect concept dependencies: each chapter builds only on concepts introduced in prior chapters. Graduate students with software architecture backgrounds may skim or skip Chapters 1–2, but all readers should complete Chapters 3–10 before tackling the applied domain chapters (11–18). The applied domain chapters (11–18) can be read in any order after Chapter 10.

Note: Each chapter index lists the concepts covered. Use the Learning Graph Viewer to explore concept dependencies visually.