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Feature Management with LaunchDarkly

You're reading from   Feature Management with LaunchDarkly Discover safe ways to make live changes in your systems and master testing in production

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781800562974
Length 314 pages
Edition 1st Edition
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Author (1):
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Michael Gillett Michael Gillett
Author Profile Icon Michael Gillett
Michael Gillett
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Table of Contents (18) Chapters Close

Preface 1. Section 1: The Basics
2. Chapter 1: Introduction to Feature Management with LaunchDarkly FREE CHAPTER 3. Chapter 2: Overview of Feature Management 4. Chapter 3: Basics of LaunchDarkly and Feature Management 5. Section 2:Getting the Most out of Feature Management
6. Chapter 4: Percentage and Ring Rollouts 7. Chapter 5: Experimentation 8. Chapter 6: Switches 9. Chapter 7: Trunk-Based Development 10. Chapter 8: Migrations and Testing Your Infrastructure 11. Section 3: Mastering LaunchDarkly
12. Chapter 9: Feature Flag Management in Depth 13. Chapter 10: Users and Segments 14. Chapter 11: Experiments 15. Chapter 12: Debugger and Audit Log 16. Chapter 13: Configuration, Settings, and Miscellaneous 17. Other Books You May Enjoy

Learning about experimentation

The Experimentation section of a feature flag was covered in detail in Chapter 5, Experimentation, so it is best to read that chapter to understand how best to use this functionality, along with Metrics. However, there are a couple of additional pieces of functionality that were not covered in that chapter that we will cover next.

When working with the experimentation functionality, it is possible that testing can distort the results of the test. Usually, the only data that should be collected and analyzed is that of the percentage or ring rollout of a flag to customers. When testers or stakeholders are validating or signing off a feature, their usage of an experiment should not impact the data. Normally, the sign-off will be performed before an experiment is presented to any customers. However, testing might continue in production (even once live) to ensure that all the variations of an experiment work. In that case, specific users or targeting rules...

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