Scientist .NET, in a nutshell, lets you test experimental code safely without exposing errors or inaccuracy to end users.
In this short and sweet opinion post, I’ll rant like a crazy man on the dangers inherent in living with compiler or linter warnings (at least I’m honest).
In this article we’ll introduce genetic algorithms by teaching a squirrel how to find food and shelter, then see how different fitness functions can influence its behavior. Along the way we’ll discuss concepts of genetic algorithms, the F# programming language, and important design considerations in artificial intelligence applications.
In this article, I’ll propose a C# solution to a common testing problem with enums using a special NUnit attribute. I’ll also introduce you to a related attribute which can expand your tests.
In this article I’ll discuss what Code Coverage is and its usefulness and limitations. I’ll advocate for a risk-aware approach to software quality and give a few practical examples in C# and F#.
In this tutorial we’ll create an ASP .NET Core 3.0 web application using MVC, Entity Framework, and a restful Web API.
In this article, we’ll implement the chromosome of a digital squirrel. Our ultimate goal is to set ourselves up for implementing a full genetic algorithm in the next article.
Let me introduce you to my go-to code visualization and analysis tool for .NET: NDepend. NDepend lets me see dependencies, issues, and quality over time.
C# 6 introduced an operator that can prevent several issues. Let’s look at the nameof operator and how it improves maintainability while reducing defects.