In just a few short years, AI has transformed software development, and snazzy new tools continue to arrive, with no let-up in sight. How, as a software engineer, product builder, or CTO, do you keep up? This practical book is the result of Sergio Pereira's mission to test every AI tool he could find and provide practitioners with much-needed guidance through the commotion.

Generative AI for Software Development focuses on AI tool comparisons, practical workflows, and real-world case studies, with each chapter encompassing critical evaluations of the tools, their use cases, and their limitations. While product reviews are always relevant, the book goes further and delivers to readers a coherent framework for evaluating the tools and workflows of the future, which will continue to complicate the work of software development.

• Learn how code generation and autocompletion assistants are reshaping the developer experience
• Discover a consistent method for rating code-generation tools based on real-world coding challenges
• Explore the GenAI tools transforming UI/UX design and frontend development
• Learn how AI is streamlining code reviews and bug detection
• Review tools that are simplifying software testing and QA
• Explore AI for documentation and technical writing
• Understand how modern LLMs have redefined what chatbots can do

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Spis treści

Generative AI for Software Development. Building Software Faster and More Effectively eBook -- spis treści

• Preface
  • How to Use This Book
  • Who This Book Is For
  • Overview of the Chapters
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Code Generation and Autocompletion
  • Types of Code-Generation Tools
  • Use Cases
  • Browser-Based Tools
    • ChatGPT
    • Google Gemini
  • IDE-Based Tools
    • GitHub Copilot
    • Cursor
    • Windsurf
  • Tool Comparison
    • ChatGPT
    • Google Gemini
    • GitHub Copilot
    • Cursor
    • Windsurf
  • Conclusion
• 2. User Interface and User Experience Design
  • Types of AI Tools for Design and Frontend Development
  • Pros and Cons of Using AI Tools for UI/UX
  • Use Cases for UI/UX AI Tools
  • Evaluation Process
  • UI Tools
    • Uizard
    • Bolt.new
    • Lovable
  • UX Tools
    • QoQo.ai
    • Research Studio
  • Tool Comparison
  • Conclusion
• 3. Bug Detection and Code Review
  • Types of AI Code-Review Tools
  • Use Cases
  • Keeping the Human Review
  • Evaluation Process
    • Codacy
      • Practical example
    • DeepCode (by Snyk)
      • Practical example
    • CodeRabbit
      • Practical example
  • Tool Comparison
  • Conclusion
• 4. Automated Testing and Quality Assurance
  • Types of AI Testing Tools
  • Use Cases
  • The Need for Human Testers
  • Evaluation Process
    • Katalon Studio
      • Practical example
      • Pros
      • Cons
    • testRigor
      • Practical example
      • Pros
      • Cons
  • Tool Comparison
  • Conclusion
• 5. Predictive Analytics and Performance Optimization
  • Data Collection and Sources
  • Use Cases for Data Analytics
  • Types of AI Tools for Data Analysis
  • Evaluation Process
    • Julius
    • Akkio
    • ChatGPT
  • Tool Comparison
  • Conclusion
• 6. Documentation and Technical Writing
  • Types of Documentation
  • Evaluation Process
    • Swimm
    • ChatGPT
    • Cursor
    • Scribe
  • Tool Comparison
  • Conclusion
• 7. Chatbots and Virtual Assistants
  • Types of Chatbot Implementations
  • Evaluation Process
    • Chatbase
    • Botpress
    • LangChain
  • Tool Comparison
  • Conclusion
• 8. Implementation Success Stories
  • Pieter Levels: Using AI Tools as an Entrepreneur
  • Shopify: Using AI Tools at a Large Enterprise
  • Beyond the Case Studies
  • Conclusion: The Future of Software Development with Generative AI
    • ATMs and Bank Tellers
    • Elevator Operators
    • Excel and Accountants
• Index