Artificial Intelligence Methods for Optimization of the Software Testing Process
With Practical Examples and Exercises
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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
Key Features
- 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
Readership
Researchers, professionals, and graduate students in computer science & engineering, applied mathematics
Table of Contents
- 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
Product details
- No. of pages: 230
- Language: English
- Copyright: © Academic Press 2022
- Published: July 21, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780323919135
- eBook ISBN: 9780323912822
About the Authors
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
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.
Affiliations and Expertise
Lecturer, Mälardalen University, Västerås, Sweden
Ratings and Reviews
Latest reviews
(Total rating for all reviews)
Ursula L. Sun Nov 20 2022
Impressive industrial case studies
Several good industrial case studies are provided in this book for the applications of AI in testing. I also found the exercises very useful for learning and practicing Python.