Artificial Intelligence Methods for Optimization of the Software Testing Process: With Practical Examples and Exercises (Uncertainty, Computational Techniques, and Decision Intelligence) - Softcover

Über die Autorinnen und Autoren

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.

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The problem of decision making under uncertainty can directly impact the obtained results and the generalizability of a proposed solution. Moreover, a complex model can sometimes act as an ‘uncertainty amplifier,’ where the uncertainty in the results is much greater than the uncertainty in the setup of the model and the data it uses. Employing decision intelligence and artificial intelligence which optimize the process of decision-making has received a great deal of attention in recent years. On the other hand, most of the real-world industrial problem scenarios suffer from volatility, uncertainty, complexity, and ambiguity. Therefore, utilizing AI-based solutions might be a time and resource-consuming process due to the problem formulation, data availability, data quality, anomalies, etc. Providing guidelines to manage diverse types of uncertainty with intelligent decision making can help the subject matter experts at industries to improve various processes in a more efficient way.

Software testing is the process of analyzing a software item with the aim to detect the differences between existing and required conditions (hidden bugs), as well as to evaluate the features of the software item. For testing a software product, several scenarios need to be created which are called test cases. 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.

In Artificial Intelligence Methods for Optimization of the Software Testing Process, the authors introduce, apply, and evaluate different AI-based solutions for overcoming the uncertainty found in many initial testing problems, and thereby solving the problem with intelligent decision making. The concept of intelligent decision making in this book is presented as a multi-criteria, multi-objective decision making problem such as scheduling a large number of test cases for execution, identifying the critical criteria (e.g. semantic and syntactic similarities between test cases, interdependencies, or requirement coverage), and measuring the impact of each criterion on the test cases automatically. Today, most of the proposed solutions for test optimization purposes are lacking in an industrial evaluation. Therefore, it is hard to employ them in real cases. However, this book will provide comprehensive industrial use cases for the proposed solutions which can easily be adapted to different testing processes across the industry. The book can also be utilized as a post-graduate reference book in the areas of artificial intelligence methods, software testing, and test optimization.

All the proposed solutions in this book employ new AI-technologies, where the details (e.g. input, parameters, code, expected output) are provided for each method. After each method, a set of practice assignments is provided for readers to verify their knowledge. Moreover, a comprehensive Appendix is provided where readers can easily use and re-run the code and solutions.