# Software Testing with AI - Volume 2: Automated Testing, Unit Testing, and Continuous Quality

Canonical URL: https://cretisoftbooks.com/en/books/automated-testing-with-ai-volume-2

Book page: https://cretisoftbooks.com/en/books/automated-testing-with-ai-volume-2

Author: Ellis Carver

Language: en

Description: Most engineering teams invest heavily in test automation tools, yet flaky tests, high maintenance costs, and coverage gaps remain persistent problems. The common advice—"write more tests"—ignores the root cause: a lack of systematic design in how tests are created, organized, and integrated into the delivery pipeline. As AI tools become part of the developer workflow, the need for a rigorous but adaptable testing strategy becomes even more critical. Software Testing with AI – Volume 2: Automated Testing, Unit Testing, and Continuous Quality by Ellis Carver provides that strategy. It's a practical, project-driven guide that moves from the basics of unit testing to building a production-ready, AI-augmented continuous quality pipeline. The book emphasizes architectural patterns and engineering trade-offs over tool-specific syntax, making it applicable to any language or framework. Key areas covered in depth: • Master the test pyramid and write robust unit tests using the Arrange-Act-Assert pattern, with techniques for testing business logic, edge cases, and exception paths. • Design integration, API, and end-to-end tests that remain stable as your system evolves, including proven strategies for eliminating flaky tests and managing dynamic data. • Integrate AI into your testing workflow to generate unit and integration tests, debug failures, and review AI-generated code, while learning to critically evaluate its output. The book is organized into five parts and fifteen chapters, each building on the previous. Part I establishes the foundation of developer testing. Part II expands to integration and end-to-end testing. Part III focuses on testable software design and continuous integration with quality gates. Part IV explores AI-assisted test development and the unique challenges of testing AI-generated applications. Part V covers production monitoring, scaling test suites, and includes a capstone project that unifies all the concepts into a real-world automation pipeline. A continuous real-world project—a sample e-commerce application—runs throughout the book, demonstrating how each testing technique applies to a concrete codebase. You'll follow the same application from its first unit test to a fully automated CI/CD pipeline with coverage thresholds and release confidence checks. This project-driven approach ensures every principle is immediately actionable. Chapters on AI for test development and testing AI-generated applications provide concrete workflows for leveraging large language models. You'll learn how to prompt LLMs to generate test code, how to verify that code for correctness, and how to avoid common pitfalls like over-reliance or false confidence. These insights are grounded in current AI capabilities and include critical review practices. This book is written for mid-level software developers, QA automation engineers, and technical leads who want to elevate their testing practices. Readers should have basic programming skills and familiarity with Git, but no prior AI experience is necessary. It's ideal for anyone who has felt their testing approach is reactive rather than strategic. By the final chapter, you'll have a blueprint for building a continuous quality system that ensures reliable releases and empowers your team to innovate with confidence.

AI summary: Software Testing with AI – Volume 2 by Ellis Carver is a project-driven guide on automated testing. It covers unit testing patterns, integration testing, API testing, end-to-end testing, and continuous quality pipelines with AI assistance. The book targets mid-level developers and QA engineers seeking to implement AI-augmented testing workflows in modern software development.

Target audience: Software developers, QA automation engineers, technical leads, and test engineers transitioning to automation.

Audience persona: A mid-level software developer or QA engineer who wants to adopt AI-assisted testing and build a systematic, scalable automated testing strategy.

Search intent: To learn practical AI-assisted test automation patterns and build a continuous quality pipeline.

Unique angle: This book uniquely combines a project-driven approach with AI-assisted testing, teaching both foundational patterns and modern AI integration for continuous quality.

Content type: developer guide

Answer snippets:
- This book teaches automated testing patterns including unit tests, integration tests, and end-to-end tests.
- It shows how to use AI to generate, debug, and review test code while maintaining engineering rigor.
- The guide is organized into five parts and fifteen chapters, including a capstone project.
- It is written for mid-level developers, QA automation engineers, and technical leads.

Key topics: Automated testing, Unit testing patterns, Integration testing, API test automation, End-to-end testing, Continuous quality, AI test generation, Test automation strategy

Entities: test pyramid, Arrange-Act-Assert, dependency injection, test doubles, CI/CD, quality gates, flaky tests, mutation testing, contract testing, property-based testing, AI test generation, continuous quality

Problems solved:
- High test maintenance costs and flaky tests
- Lack of systematic test design and coverage gaps
- Integrating AI into existing testing workflows
- Transitioning from manual to automated testing
- Building reliable test suites for microservices and distributed systems

Who should read:
- Software developers
- QA automation engineers
- Software test engineers transitioning to automation
- Technical leads
- Developers using AI coding assistants

Who should not read:
- Beginners in programming
- Non-technical roles
- Readers looking for tool-specific tutorials
- Those seeking theoretical testing knowledge without hands-on practice

FAQ:

Q: What is the main focus of this book?
A: It focuses on automated testing, unit testing, and continuous quality, with practical guidance on integrating AI into test development.

Q: Who is this book for?
A: It is designed for mid-level software developers, QA automation engineers, and technical leads who want to improve their automated testing practices.

Q: Does it require prior AI experience?
A: No, no prior AI experience is necessary; the book introduces AI concepts gradually.

Q: Is it framework-specific?
A: The book is framework-agnostic, emphasizing principles over specific tools.

Q: What type of project is used?
A: A sample e-commerce application is used throughout the book to demonstrate testing techniques.

SEO keywords: automated testing with AI, unit testing patterns, continuous quality, AI test generation, test automation strategy, flaky test mitigation, software testing book, QA automation guide, AI-assisted testing, integration testing patterns

Table of contents:
- Introduction
- Modern Developer Testing
  - From Manual Testing to Test Automation
    - Why Automation Matters
    - The Test Pyramid
    - Choosing the Right Level of Testing
    - ROI of Test Automation
    - AI-Assisted Test Automation
  - Writing Effective Unit Tests
    - What Makes a Good Unit Test
    - Arrange–Act–Assert
    - Test Fixtures
    - Assertions
    - Common Mistakes
  - Testing Business Logic
    - Pure Functions
    - Domain Rules
    - Exception Testing
    - Parameterized Tests
    - Edge Cases
- Integration Testing
  - Integration Testing
    - Databases
    - External APIs
    - File Storage
    - Authentication
    - Background Jobs
  - API Test Automation
    - Organizing API Tests
    - Test Data Management
    - Environment Management
    - Regression Suites
    - Reporting Results
  - End-to-End Testing
    - User Workflows
    - Stable Test Design
    - Handling Dynamic Data
    - Flaky Tests
    - Maintaining E2E Tests
- Building Reliable Software
  - Testable Software Design
    - Dependency Injection
    - Separation of Concerns
    - Test Doubles
    - Refactoring for Testability
    - Legacy Code
  - Continuous Testing
    - Continuous Integration
    - Quality Gates
    - Test Coverage
    - Test Reports
    - Release Confidence
  - Advanced Testing Techniques
    - Contract Testing
    - Mutation Testing
    - Property-Based Testing
    - Consumer-Driven Testing
    - Testing Distributed Systems
- AI-Assisted Testing
  - AI for Test Development
    - AI-Generated Unit Tests
    - AI-Generated Integration Tests
    - AI Code Review
    - AI Debugging
    - Reviewing AI Output
  - Testing AI-Generated Applications
    - Verifying AI Code
    - Regression for AI Projects
    - AI Testing Workflows
    - Human Review
    - Building Trust
- Production Quality
  - Testing in Production
    - Smoke Tests
    - Monitoring
    - Logging
    - Error Tracking
    - Incident Response
  - Scaling Test Automation
    - Large Test Suites

Sample EPUB: https://cretisoftbooks.com/book-samples/6a62456520471dc2c7a52fcf-1785296464415-software-testing-with-ai-volume-2-automated-testing-unit-testing-and-continuous-quality-epub-mau-20.epub

Purchase links:
- Google Books: https://play.google.com/store/books/details?id=nsL4EQAAQBAJ