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Full Description
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
Contents
PART 1 Software testing, artificial intelligence, decision intelligence, and test optimization
1. Introduction
2. Basic software testing concepts
3. Transformation, vectorization, and optimization
4. Decision intelligence and test optimization
5. Application of vectorized test artifacts
6. Benefits, results, and challenges of artificial intelligence
7. Discussion and concluding remarks
PART 2 Practical examples and exercises
8. Environment installation
9. Exercises
Appendix A. Ground truth, data collection, and annotation
