Artificial Intelligence Methods for Optimization of the Software Testing Process: With Practical Examples and Exercises presents different AI-based solutions for overcoming the uncertainty found in many initial testing problems. The concept of intelligent decision making is presented as a multi-criteria, multi-objective undertaking. The book provides guidelines on how to manage diverse types of uncertainty with intelligent decision-making that can help subject matter experts in many industries improve various processes in a more efficient way.
As the number of required test cases for testing a product can be large (in industry more than 10,000 test cases are usually created). Executing all these test cases without any particular order can impact the results of the test execution, hence this book fills the need for a comprehensive resource on the topics on the how's, what's and whys.
To learn more about Elsevier’s Series, Uncertainty, Computational Techniques and Decision Intelligence, please visit this link: https://www.elsevier.com/books-and-journals/book-series/uncertainty-computational-techniques-and-decision-intelligence
1. Chapter One : Introduction - p. Page xx 2. Chapter Two : Basic software testing concepts - p. Page 6 3. Chapter Three : Transformation, vectorization, and optimization - p. Page 30 4. Chapter Four : Decision intelligence and test optimization - p. Page 78 5. Chapter Five : Application of vectorized test artifacts - p. Page 103 6. Chapter Six : Benefits, results, and challenges of artificial intelligence - p. Page 160 7. Chapter Seven : Discussion and concluding remarks - p. Page 171 8. Chapter Eight : Environment installation - p. Page 174 9. Chapter Nine : Exercises - p. Page 179 10. Appendix A : Ground truth, data collection, and annotation - p. Page 185 11. Index - p. Page 196
Artificial Intelligence Methods for Optimization of the Software Testing Process: With Practical Examples and Exercises presents different AI-based solutions for overcoming the uncertainty found in many initial testing problems. The concept of intelligent decision making is presented as a multi-criteria, multi-objective undertaking. The book provides guidelines on how to manage diverse types of uncertainty with intelligent decision-making that can help subject matter experts in many industries improve various processes in a more efficient way.
As the number of required test cases for testing a product can be large (in industry more than 10,000 test cases are usually created). Executing all these test cases without any particular order can impact the results of the test execution, hence this book fills the need for a comprehensive resource on the topics on the how's, what's and whys.
To learn more about Elsevier’s Series, Uncertainty, Computational Techniques and Decision Intelligence, please visit this link: https://www.elsevier.com/books-and-journals/book-series/uncertainty-computational-techniques-and-decision-intelligence
1. Chapter One : Introduction - p. Page xx 2. Chapter Two : Basic software testing concepts - p. Page 6 3. Chapter Three : Transformation, vectorization, and optimization - p. Page 30 4. Chapter Four : Decision intelligence and test optimization - p. Page 78 5. Chapter Five : Application of vectorized test artifacts - p. Page 103 6. Chapter Six : Benefits, results, and challenges of artificial intelligence - p. Page 160 7. Chapter Seven : Discussion and concluding remarks - p. Page 171 8. Chapter Eight : Environment installation - p. Page 174 9. Chapter Nine : Exercises - p. Page 179 10. Appendix A : Ground truth, data collection, and annotation - p. Page 185 11. Index - p. Page 196
