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
- Presents one of the first empirical studies in the field, contrasting theoretical assumptions on innovations in a real industrial environment with a large set of use cases from developed and developing testing processes at various large industries
- Explores specific comparative methodologies, focusing on developed and developing AI-based solutions
- Serves as a guideline for conducting industrial research in the artificial intelligence and software testing domain
- Explains all proposed solutions through real industrial case studies
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
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. Sahars 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.
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