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

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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 hows, whats and whys.

To learn more about Elseviers 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

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Artificial Intelligence Methods for Optimization of the Software Testing - photo 1
Artificial Intelligence Methods for Optimization of the Software Testing Process
With Practical Examples and Exercises

First edition

Sahar Tahvili

Product Development Unit, Cloud RAN, Integration and Test, Ericsson AB, Stockholm, Sweden

Division of Product Realisation, School of Innovation, Design and Engineering, Mlardalen University, Eskilstuna, Sweden

Leo Hatvani

Division of Product Realisation, School of Innovation, Design and Engineering, Mlardalen University, Eskilstuna, Sweden

Table of Contents Table of Contents List of tables Tables in Chapter Two - photo 2

Table of Contents
Table of Contents
List of tables
  1. Tables in Chapter Two
  2. Tables in Chapter Three
  3. Tables in Chapter Four
  4. Tables in Chapter Five
  5. Tables in Appendix A
List of figures
  1. Figures in Chapter Two
  2. Figures in Chapter Three
  3. Figures in Chapter Four
  4. Figures in Chapter Five
  5. Figures in Chapter Six
  6. Figures in Appendix A
Landmarks

Part One: Software testing, artificial intelligence, decision intelligence, and test optimization

Outline

Part Two: Practical examples and exercises

Outline

Copyright

Academic Press is an imprint of Elsevier

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Copyright 2022 Elsevier Inc. All rights reserved.

No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher's permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions.

This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein).

Notices

Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary.

Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.

To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein.

ISBN: 978-0-323-91913-5

For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Mara Conner

Editorial Project Manager: John Leonard

Production Project Manager: Swapna Srinivasan

Designer: Mark Rogers

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Dedication I would like to dedicate this book to my sister Sara and my niece - photo 3

Dedication

I would like to dedicate this book to my sister Sara and my niece Nilsa, who have always stood by me every step of the way with unconditional love and support.

Sahar

I would like to dedicate this book to my parents who have always encouraged me on my science journeys.

Leo

List of figures
  1. Figure 2.1 A customized version of a software development life cycle (SDLC). Source: Authors' own contribution, inspired by the original model of the software development life cycle.
  2. Figure 2.2 A customized version of a software testing life cycle model. Source: Authors' own contribution, inspired by the original model of the software testing life cycle.
  3. Figure 2.3 The V-model in software testing. Source: Authors' own contribution, inspired by the original V-model.
  4. Figure 2.4 A Venn diagram of the software test artifacts. The red zone represents a manual testing process, whereas the green zone indicates an automated (or semi-automated) testing process. Source: Authors' own contribution.
  5. Figure 2.5 An example of a test summary report. Source: Authors' own contribution.
  6. Figure 2.6 Test execution analysis using the test summary report. Source: Authors' own contribution.
  7. Figure 2.7 A summary of the evolution of software testing. Source: Authors' own contribution.
  8. Figure 3.1 The stages of the text analytics practice from World War II until the 1990s. Source: Authors' own contribution.
  9. Figure 3.2 The key concepts and rules of transformational grammar (also called transformational-generative grammar [TGG]). Source: Authors' own contribution inspired by Adetuyi and Fidelis [4].
  10. Figure 3.3 The structure of symbolic modeling. Source: Authors' own contribution.
  11. Figure 3.4 The stages of text analytics in the artificial intelligence era. Source: Authors' own contribution, inspired by Krippendorff [3].
  12. Figure 3.5 An overview of the sub-disciplines and aspects of text analytics and how they relate. Source: Authors' own contribution, inspired by Miner et al. [1].
  13. Figure 3.6 An example of the inverted index structure using the segment Set the critical relays status into the ATP-ATO Propulsion and Brake screens from the presented test case in
  14. Figure 3.7 Sub-disciplines of web mining. Source: Authors' own contribution, inspired by Shah and Pandit [19].
  15. Figure 3.8 An example of text classification. The model is used to flag incoming emails as spam. Source: Authors' own contribution.
  16. Figure 3.9 An example of hard and soft text clustering, where C1 Picture 4 is an example of hard clustering and C2 Picture 5 and C3 Picture 6 are two examples of soft clustering. Source: Authors' own contribution.
  17. Figure 3.10 Process of concept extraction. Source: Authors' own contribution, inspired by Tulkens et al. [25].
  18. Figure 3.11 An example pipeline of natural language generation. Source: Authors' own contribution, inspired by the proposed frameworks by Reiter and Dale [32] and Banaee et al. [30].
  19. Figure 3.12 An example information extraction template for the DiscoTEX approach. Source: Authors' own contribution.
  20. Figure 3.13 Representation of the TF-IDF calculation for the individual words of a text. Source: Authors' own contribution.
  21. Figure 3.14 The structure of the continuous bag of words (CBoW) model. Source: Mikolov et al. [81].
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