Source Code Analytics With Roslyn and JavaScript Data Visualization

Source Code Analytics With Roslyn and JavaScript Data Visualization
Author: Sudipta Mukherjee
Pub Date: 2017
ISBN: 978-1-4842-1924-9
Pages: 170
Language: English
Format: PDF/EPUB
Size: 10 Mb

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Learn how to build an interactive source code analytics system using Roslyn and JavaScript. This concise 150 page book will help you create and use practical code analysis tools utilizing the new features of Microsoft’s Roslyn compiler to understand the health of your code and identify parts of the code for refactoring.
Source code is one of the biggest assets of a software company. However if not maintained well, it can become a big liability. As source code becomes larger. more complex and accessed via the cloud, maintaining code quality becomes even more challenging. The author provides straightforward tools and advice on how to manage code quality in this new environment.
Roslyn exposes a set of APIs which allow developers to parse their C# and VB.NET code and drastically lower the barrier to entry for Meta programming in .NET. Roslyn has a dedicated set of APIs for creating custom refactoring for integrating with Visual Studio.
This title will show readers how to use Roslyn along with industry standard JavaScript visualization APIs like HighCharts, D3.js etc to create a scalable and highly responsive source code analytics system.
What You Will Learn

  • Understand the Roslyn Syntax API
  • Use Data Visualization techniques to assist code analysis process visually
  • Code health monitoring matrices (from the standard of Code Query Language)
  • Code mining techniques to identify design patterns used in source code
  • Code forensics techniques to identify probable author of a given source code
  • Techniques to identify duplicate/near duplicate code

Who This Book is For
.NET Software Developers and Architects

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Table of Contents

1: Meet Roslyn Syntax API
2: Code Quality Metrics
3: Design Quality Metrics
4: Code Performance Metrics
5: Code Mining
6: Code Forensics
7: Code Visualization