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Artificial Intelligence Methods for Optimization of the Software Testing Process
With Practical Examples and Exercises
1st Edition - July 21, 2022
Authors: Sahar Tahvili, Leo Hatvani
Paperback ISBN:9780323919135
9 7 8 - 0 - 3 2 3 - 9 1 9 1 3 - 5
eBook ISBN:9780323912822
9 7 8 - 0 - 3 2 3 - 9 1 2 8 2 - 2
Artificial Intelligence Methods for Optimization of the Software Testing Process: With Practical Examples and Exercises presents different AI-based solutions for overcoming the… Read more
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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
Researchers, professionals, and graduate students in computer science & engineering, applied mathematics
Cover image
Title page
Table of Contents
Copyright
Dedication
List of figures
List of tables
Biography
Preface
Acknowledgments
Part One: Software testing, artificial intelligence, decision intelligence, and test optimization
Chapter One: Introduction
Abstract
1.1. Our digital era for a better future
1.2. What is in this book?
1.3. What is missing?
Chapter Two: Basic software testing concepts
Abstract
2.1. Software development life cycle
2.2. Software testing
2.3. Test artifacts
2.4. The evolution of software testing
References
Chapter Three: Transformation, vectorization, and optimization
Abstract
3.1. A review of the history of text analytics
3.2. Text transformation and representation
3.3. Vectorization
3.4. Imbalanced learning
3.5. Dimensionality reduction and visualizing machine learning models
References
Chapter Four: Decision intelligence and test optimization
Abstract
4.1. The evolution of artificial intelligence
4.2. Decision-making in a VUCA world
4.3. Multi-criterion intelligent test optimization methodology
4.4. Static and continuous test optimization process
References
Chapter Five: Application of vectorized test artifacts
Abstract
5.1. Test artifact optimization using vectorization and machine learning
5.2. Vectorization of requirements specifications
5.3. Vectorization of test case specifications
5.4. Vectorization of test scripts
5.5. Vectorization of test logs
5.6. Implementation
References
Chapter Six: Benefits, results, and challenges of artificial intelligence
Abstract
6.1. Benefits and barriers to the adoption of artificial intelligence
6.2. Artificial intelligence platform pipeline
6.3. Costs of artificial intelligence integration into the software development life cycle
References
Chapter Seven: Discussion and concluding remarks
Abstract
7.1. Closing remarks
Part Two: Practical examples and exercises
Chapter Eight: Environment installation
Abstract
8.1. JupyterLab installation
8.2. GitHub labs
Chapter Nine: Exercises
Abstract
9.1. Python exercises and practice
9.2. Exercise 1: Data processing
9.3. Exercise 2: Natural language processing techniques
9.4. Exercise 3: Clustering
9.5. Exercise 4: Classification
9.6. Exercise 5: Imbalanced learning
9.7. Exercise 6: Dimensionality reduction and visualization
References
Appendix A: Ground truth, data collection, and annotation
A.1. Ground truth
References
Index
No. of pages: 230
Language: English
Published: July 21, 2022
Imprint: Academic Press
Paperback ISBN: 9780323919135
eBook ISBN: 9780323912822
ST
Sahar Tahvili
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.
Affiliations and expertise
Operations Team Leader, Ericsson AB and Researcher, Mälardalen University, Västerås, Sweden
LH
Leo Hatvani
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.