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C#/VB .NET Coding Guidelines

Posted Date: 24 Dec 2010 |Updated: 24-Dec-2010 |Category: C# Book Review |Author: |Member Level: Silver |Points: 2 |


.NET Coding Guidelines is a 100+ page ebook (PDF) on naming conventions, best coding practices and patterns written by the industry expert Steven Sartain and delivered to you for Free by SubMain.

The document covers:

Naming Guidelines
Class Member Usage Guidelines
Guidelines for Exposing Functionality to COM
Error Raising & Handling Guidelines
Array Usage Guidelines
Operator Overloading Usage Guidelines
Guidelines for Casting Types
Common Design Patterns
Callback Function Usage
Time-Out Usage
Security in Class Libraries
Threading Design Guidelines
Formatting Standards
Commenting Code
Code Reviews
Additional Notes for VB .NET Developers

Reference: http://submain.com/products/guidelines.aspx


Attachments

  • C#/VB .NET Coding Guidelines (41690-242328-Submain_DotNET_Coding_Guidelines.zip)

    • Comments

    Author: sarang13 Oct 2011 Member Level: Bronze   Points : 0

    gr8 comparison

    Guest Author: Urdea11 Jun 2012

    I don't know of an article related to this, but in signal processing a LaPlace transform is used all the time for transient analysis of signals, as well as the convolution theorem which is derived from the inverse LaPlace transform. Basically if you have a system with some initial stuff and you actually care about what it's going to start with, then a LaPlace transform can be used to predict what exactly will happen when the system is hit with an input. This is used all the time in control system design.The Fourier transform is related to the LaPlace transform, but it is a special case that ignores the transient (initial) response and jumps straight into what it's going to do after a long time. It's called frequency domain analysis and is the primary mathematical tool in signals analysis.In probability, moment generating functions are frequently used to analyze the random behavior of a system, and the moment generating function is essentially the LaPlace transform of the probability density function. By calculating the moments of a sample of random data, you essentially get the coefficients for each term in the Taylor's series expansion of the moment generating function, and then you can either identify the series on a table or numerically compute the inverse LaPlace transform of the moment generating function and you thereby have a good estimate of the probability density function of the system you're looking at. This is used all the time in engineering and also in experimental physics and chemistry.



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