Software Engineering in the AI Era: Rethinking Code Review, Testing, Architecture, Productivity, and Developer Workflows for AI-Assisted Engineering - Devlin, Mira S.
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Software Engineering in the AI Era: Rethinking Code Review, Testing, Architecture, Productivity, and Developer Workflows for AI-Assisted Engineering
Devlin, Mira S.
Synopsis "Software Engineering in the AI Era: Rethinking Code Review, Testing, Architecture, Productivity, and Developer Workflows for AI-Assisted Engineering"
Amazon Book Description AI can write code faster. But can your engineering process keep up? Software development is entering a fundamental transition. AI coding assistants and autonomous development tools can generate code, tests, refactoring plans, documentation, and multi-file implementations at a speed that changes the economics of software production. But faster code generation does not automatically produce better software. Software Engineering in the AI Era examines what happens when implementation becomes dramatically easier—and why code review, testing, architecture, productivity measurement, and developer workflows must evolve with it. Mira S. Devlin presents a practical framework for engineering teams navigating AI-assisted development. Rather than treating AI as either a replacement for developers or simply a faster autocomplete tool, this book examines AI as a change to the entire software engineering system. You will explore how to: Rethink code review when AI can generate substantial implementations Build testing strategies that provide evidence rather than simply more test code Preserve architectural boundaries as implementation becomes cheaper Identify technical debt and emerging comprehension debt Evaluate AI-assisted productivity without confusing output with engineering value Design developer workflows around appropriate human checkpoints Improve onboarding and knowledge transfer in AI-assisted teams Understand the risks of generated assumptions, hidden dependencies, and plausible but incorrect implementations Adapt engineering practices without abandoning proven software engineering fundamentals Build workflows that balance development speed with correctness, maintainability, and accountability The central idea is simple: When code becomes cheaper to produce, engineering judgment becomes more valuable. AI can generate an implementation. It cannot remove the engineering responsibility to determine whether that implementation is correct, appropriate, understandable, and safe to operate. This book is for software engineers, senior developers, architects, engineering managers, technical leaders, and teams adopting AI-assisted development who want to increase their leverage without allowing development speed to outrun engineering understanding. The future of software engineering is not simply about writing more code. It is about building better systems with greater leverage—and knowing what still requires human judgment.
