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Software Testing Strategies

You're reading from   Software Testing Strategies A testing guide for the 2020s

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Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781837638024
Length 378 pages
Edition 1st Edition
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Authors (2):
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Matthew Heusser Matthew Heusser
Author Profile Icon Matthew Heusser
Matthew Heusser
Michael Larsen Michael Larsen
Author Profile Icon Michael Larsen
Michael Larsen
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Toc

Table of Contents (22) Chapters Close

Preface 1. Part 1:The Practice of Software Testing
2. Chapter 1: Testing and Designing Tests FREE CHAPTER 3. Chapter 2: Fundamental Issues in Tooling and Automation 4. Chapter 3: Programmer-Facing Testing 5. Chapter 4: Customer-Facing Tests 6. Chapter 5: Specialized Testing 7. Chapter 6: Testing Related Skills 8. Chapter 7: Test Data Management 9. Part 2:Testing and Software Delivery
10. Chapter 8: Delivery Models and Testing 11. Chapter 9: The Puzzle Pieces of Good Testing 12. Chapter 10: Putting Your Test Strategy Together 13. Chapter 11: Lean Software Testing 14. Part 3:Practicing Politics
15. Chapter 12: Case Studies and Experience Reports 16. Chapter 13: Testing Activities or a Testing Role? 17. Chapter 14: Philosophy and Ethics in Software Testing 18. Chapter 15: Words and Language About Work 19. Chapter 16: Testing Strategy Applied 20. Index 21. Other Books You May Enjoy

This chapter is completely wrong

If you took a stopwatch and looked at what a skilled tester does, actually executing the test is only a small portion of the job. As mentioned previously, test execution automation doesn’t include test design. Test design, however, can be assisted with tools. The consultant Michael Bolton recently wrote about ways to automate parts of the test design process (https://developsense.com/blog/2023/01/test-tools).

In test design, we use tools to help us achieve the following:

  • Produce test data [tools like spreadsheets (why are some testers so eager to diss Excel? It’s like a Swiss Army knife for testing); state-model generators; Monte Carlo simulations; random number generators].
  • Obfuscate or cleanse production data for privacy reasons (data shufflers; name replacers), generate interesting combinations of parameters (all-pairs or combinatorial data generators), and generate flows through the product that cover specific conditions...
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