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AI-Augmented Software Engineering

Tools, Practices, and Ethics

Imagine writing production-grade code with fewer errors, sprint reviews that highlight real insights instead of guesses, and teams that spend less time debugging and more time innovating. This isn’t science fiction, it’s the future of software engineering.

AI-Augmented Software Engineering unlocks the practical power of AI tools while grounding you in ethical best practices. Discover how to automate the mundane, amplify your creativity, and shape intelligent systems that serve users all without sacrificing trust, quality, or integrity.

Welcome to the next wave of engineering designed with intelligence, driven by values.

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About

About

About the Book

AI-Augmented Software Engineering: Tools, Practices, and Ethics explores how artificial intelligence is reshaping the craft of building modern software from ideation to deployment and beyond. This book bridges cutting-edge AI tools with sound engineering discipline, giving both individual practitioners and team leaders a practical roadmap for embedding AI into everyday engineering workflows.


Learn how to leverage AI companions to write better code, automate repetitive tasks, boost team productivity, and improve software quality without compromising ethical standards, privacy, reliability, or accountability. Through real examples, forward-thinking practices, and thoughtful discussions on ethics and governance, this book prepares you for the next era of software engineering: one where human ingenuity and intelligent machines collaborate.


Whether you’re a software engineer, team lead, CTO, or AI enthusiast, this book equips you with the strategies, tools, and frameworks needed to build software responsibly and at scale.

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Author

About the Author

Goziechukwu Chima-Duru

Goziechukwu Chima-Duru is a Senior Software and DevOps Engineer focused on building scalable, resilient, and production-grade systems. His work spans distributed architecture, cloud infrastructure, CI/CD automation, reliability engineering, and organizational design for high-performing engineering teams.

With experience navigating cross-regional deployments, compliance-sensitive environments, and high-growth technical platforms, he approaches software engineering as both a technical and strategic discipline. His writing connects system design, operational resilience, governance, and engineering culture emphasizing principles that endure beyond specific tools or trends.

He writes for engineers who operate in production and leaders responsible for long-term technical direction.

Contents

Table of Contents

Part I: The Shift to AI-Assisted Engineering Chapter 1: The Evolution of Software Development From manual coding to intelligent assistance Automation vs augmentation Why AI is changing how software is built Limits of traditional engineering workflows Chapter 2: Foundations of AI in Software Engineering Machine learning concepts engineers must understand Large language models and code intelligence Training data, inference, and feedback loops Strengths and failure modes of AI systems Part II: AI in the Developer Workflow Chapter 3: AI-Assisted Coding and Refactoring Code generation and completion Refactoring legacy systems with AI Prompt engineering for developers Preventing over-reliance and hallucinated code Chapter 4: Intelligent Code Review and Quality Assurance Automated code review systems Static analysis enhanced by ML Detecting security vulnerabilities with AI Balancing speed with correctness Chapter 5: AI-Driven Testing Strategies Test case generation and prioritization Mutation testing and fault prediction Test maintenance with evolving codebases Limitations of AI-generated tests Part III: AI in DevOps and System Operations Chapter 6: AIOps and Intelligent Observability Predictive monitoring and anomaly detection Log analysis and root cause inference Noise reduction in alerts Human-in-the-loop operations Chapter 7: Autonomous Deployment and Release Engineering AI-guided deployment strategies Canary analysis and automated rollbacks Feature experimentation with AI insights Risks of self-directed systems Chapter 8: Self-Healing and Adaptive Systems Automated incident response Feedback-driven remediation Reinforcement learning in operations Guardrails for autonomous behavior Part IV: Human–AI Collaboration Chapter 9: Redefining the Role of the Software Engineer Skill shifts in the AI era Engineering judgment vs machine output Maintaining craftsmanship with AI tools Career paths and specialization Chapter 10: Team Dynamics and Productivity with AI Measuring productivity beyond output AI pair programming models Knowledge sharing and onboarding Preventing skill atrophy Part V: Ethics, Security, and Governance Chapter 11: Trust, Bias, and Accountability Bias in training data and outputs Explainability and transparency Accountability for AI-generated code Legal and intellectual property concerns Chapter 12: Security Risks in AI-Driven Development Prompt injection and model manipulation Supply-chain risks in AI tools Secure use of AI in sensitive systems Defending against malicious AI use Chapter 13: Governance Frameworks and Policy Responsible AI guidelines for engineering teams Compliance and regulatory considerations Model evaluation and approval processes Auditability of AI-assisted decisions Part VI: Building and Integrating AI Engineering Tools Chapter 14: Designing AI-Native Developer Platforms Toolchain integration and developer experience Build vs buy decisions Data pipelines for AI feedback Evaluating ROI of AI adoption Chapter 15: Custom Models and Domain-Specific AI Fine-tuning for internal codebases Private models and data isolation Embedding AI into enterprise workflows Performance and cost trade-offs Part VII: Case Studies and the Road Ahead Chapter 16: Real-World Implementations and Failures Successful AI-augmented engineering teams Lessons from failed AI integrations Metrics that matter in practice Chapter 17: The Future of Software Engineering in an AI World Co-evolution of humans and machines Fully autonomous development: myth or reality Long-term impacts on education and hiring Preparing engineers for continuous disruption

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