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Refactoring with C#

You're reading from   Refactoring with C# Safely improve .NET applications and pay down technical debt with Visual Studio, .NET 8, and C# 12

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Product type Paperback
Published in Nov 2023
Publisher
ISBN-13 9781835089989
Length 434 pages
Edition 1st Edition
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Author (1):
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Matt Eland Matt Eland
Author Profile Icon Matt Eland
Matt Eland
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Table of Contents (24) Chapters Close

Preface 1. Part 1: Refactoring with C# in Visual Studio FREE CHAPTER
2. Chapter 1: Technical Debt, Code Smells, and Refactoring 3. Chapter 2: Introduction to Refactoring 4. Chapter 3: Refactoring Code Flow and Iteration 5. Chapter 4: Refactoring at the Method Level 6. Chapter 5: Object-Oriented Refactoring 7. Part 2: Refactoring Safely
8. Chapter 6: Unit Testing 9. Chapter 7: Test-Driven Development 10. Chapter 8: Avoiding Code Anti-Patterns with SOLID 11. Chapter 9: Advanced Unit Testing 12. Chapter 10: Defensive Coding Techniques 13. Part 3: Advanced Refactoring with AI and Code Analysis
14. Chapter 11: AI-Assisted Refactoring with GitHub Copilot 15. Chapter 12: Code Analysis in Visual Studio 16. Chapter 13: Creating a Roslyn Analyzer 17. Chapter 14: Refactoring Code with Roslyn Analyzers 18. Part 4: Refactoring in the Enterprise
19. Chapter 15: Communicating Technical Debt 20. Chapter 16: Adopting Code Standards 21. Chapter 17: Agile Refactoring 22. Index 23. Other Books You May Enjoy

Case study – Cloudy Skies Airlines

As we close the chapter, let’s look at our case study from Cloudy Skies Airlines.

Brian, a lead developer, has been investigating a growing number of problems with the reservation and payment processing part of the application.

These issues, initially thought to be isolated, seem to occur during peak usage times when many customers are trying to book flights or modify their existing flight reservations.

After investigating, Brian and his team discover that the problems are related to the current design and architecture of the system. While the system could handle the old number of users, it is simply not able to adequately scale to handle peak workloads given its current inefficiencies.

Ordinarily, such a system could be scaled out to have multiple servers running in parallel with a load balancer distributing traffic between them (see Figure 15.4):

Figure 15.4 – A load balancer distributing requests to different application servers

Figure 15.4 – A load balancer distributing...

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