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Software testing is expensive. Through up-front and recurring investment in labor and assets, organizations spend roughly half of their engineering budget on testing activities. The good news is that automated testing efforts have begun to improve through recent advances in AI and machine learning. With this report, you'll learn how AI-driven approaches are helping organizations overcome problems that traditional automated testing tools can't touch.

Author Tariq King, chief scientist at test.ai, helps you examine several AI-driven solutions for software testing issues. You'll explore the application of AI and ML to functional, structural performance, and user design testing, and then dive into techniques for automating graphically intensive solutions such as video streaming and gaming applications. You'll learn the current benefits and limitations of AI-driven testing, available applications, and insights into the future of this new and emerging discipline.

  • Learn the challenges and limitations of traditional approaches to software test automation
  • Explore how AI is advancing the state of the art in automated functional, structural, performance, and user design testing in several application domains
  • Examine the costs, benefits, and possible applications of AI-driven testing
  • Look at ways that AI-driven test automation tools will evolve over the next decade

Table of Contents

  1. Preface
    1. Who This Report Is For
    2. What You Will Learn
  2. 1. The Test Automation Gap
    1. The Human Element of Software Testing
    2. Grand Challenges
    3. Input/Output
    4. State Explosion
    5. Data
    6. The Oracle
    7. Environment
    8. Limitations of Traditional Approaches
    9. UI Test Automation
    10. Service and Unit Test Automation
    11. Nonfunctional Test Automation
    12. Conclusion
  3. 2. Leveraging AI for Test Automation
    1. AI for UI Testing
    2. Perceive
    3. Explore and Model
    4. Test
    5. Learn
    6. AI for Service/API Testing
    7. AI for Unit Testing
    8. AI for Performance Testing
    9. Application Performance Benchmarking
    10. Toward End-to-End Performance Testing with AI
    11. AI for Design Testing
    12. AI for Mobile Design Testing
    13. AI for Web Accessibility Testing
    14. AI for UI Trustworthiness Testing
    15. Conclusion
  4. 3. Automating Graphically Intensive Apps
    1. AI for Video Stream Testing
    2. Video Stream Testing Practices
    3. Automating Video Quality Testing with AI
    4. AI for Game Testing
    5. Video Game–Testing Practices
    6. Testing Game Applications with AI
    7. Testing Game Stores with AI
    8. Testing Gaming Assets with AI
    9. Gameplay Testing with AI
    10. Conclusion
  5. 4. Costs and Benefits of AI-Driven Testing
    1. Investment Costs
    2. Homegrown Solutions
    3. Vendor-Based Solutions
    4. ROI
    5. Practical Benefits
    6. Increased Coverage and Acceleration
    7. Reuse and Scalability
    8. Robustness and Resiliency
    9. Impact on Total Costs
    10. Conclusion
  6. 5. Future Directions
    1. Enhancing Existing Tools
    2. Full Stack Replacement
    3. Self-Testing Adaptive AI
    4. Conclusion
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