Table of 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

About the Author

Sahar Tahvili is an Operations Team Leader in the Product Development Unit, Cloud RAN, Integration, and Test at Ericsson AB, and also a Researcher at Mälardalen University. Sahar holds a Ph.D. in Software Engineering from Mälardalen University. Her doctoral thesis entitled "Multi-Criteria Optimization of System Integration Testing" was named one of the best new Software Integration Testing books by BookAuthority. She earned her B.S and M.S. in Applied Mathematics with an emphasis on optimization. Sahar’s research focuses on artificial intelligence (AI), advanced methods for testing complex software-intensive systems, and designing decision support systems (DSS). Previously she worked as a senior researcher at the Research Institutes of Sweden and as a senior data scientist at Ericcson AB. Leo Hatvani is a Lecturer at Mälardalen University. Leo holds a Licentiate degree in the verification of embedded systems from Mälardalen University. His current research focuses on artificial intelligence (AI) and advanced methods for testing complex software-intensive systems. His teaching is focused on improving Industry 4.0 production processes and product development by integrating artificial intelligence, augmented and virtual reality. He is working closely with Mälardalen Industrial Technology Centre (MITC) which cooperates with a number of regional companies to introduce Industry 4.0 practices into Swedish industry.