Giter Site home page Giter Site logo

nunit.analyzers's Introduction

NUnit Analyzers

Build status MyGet Feed

This is a suite of analyzers that target the NUnit testing framework. Right now, the code is separate from the NUnit framework, so if you want to try out the analyzers you'll need to download the code, build it and then use the generated NuGet package in your project. In the future the analyzers may be added as part of the NUnit framework package but that hasn't been done right now.

Building

First, make sure you have the right tools and templates on your machine. You'll need Visual Studio 2017 and the .NET Compiler Platform SDK. The .NET Compiler Platform SDK can be installed via the Visual Studio Installer. Either

  • check the Visual Studio extension development workload; open the Visual Studio extension development node in the summary tree to the right; and check the box for .NET Compiler Platform SDK (last under the optional components), or
  • select the Individual components tab and check the box for .NET Compiler Platform SDK (at the top under the Compilers, build tools, and runtimes section).

The code can now be compiled using the cake script using .\build.ps1 or from within Visual Studio. From Visual Studio one can also debug the analyzer by setting nunit.analyzers.vsix project as StartUp project and press F5 (Start Debugging). This will compile the analyzer as an extension and start a new (experimental) instance of Visual Studio with the extension.

One can also pack a NuGet package using the cake script via .\build.ps1 -Target Pack. This will create a NuGet package under package\Debug\ (for a Debug build) and the file will be named NUnit.Analyzers.***.nupkg where *** depends upon the build type (Debug vs. Release) and the version. The NuGet package can then be referenced from another project.

See NuGet package vs. extension for more information about the difference between installing a Roslyn analyzer as a NuGet package or as a Visual Studio extension.

Analyzers

Right now there are two analyzers in the code base. One will look for methods with the [TestCase] attribute and makes sure the argument values are correct for the types of the method parameters along with the ExpectedResult value if it was provided. testcase

The other analyzer looks for classic model assertions (e.g. Assert.AreEqual(), Assert.IsTrue()) and change them into constraint model assertions (Assert.That()). classicmodelassertions

Download

Prerelease nuget packages can be found on MyGet. Please try out the package and report bugs and feature requests.

License

NUnit analyzers are Open Source software and released under the MIT license, which allow the use of the analyzers in free and commercial applications and libraries without restrictions.

Contributing

There are several ways to contribute to this project. One can try things out, report bugs, propose improvements and new functionality, work on issues (especially the issues marked with the labels help wanted and Good First Issue), and in general join in the conversations. See Contributing for more information.

This project has adopted the Code of Conduct from the Contributor Covenant, version 1.4, available at http://contributor-covenant.org/version/1/4. See the Code of Conduct for more information.

Contributors

NUnit.Analyzers was created by Jason Bock. A complete list of contributors can be found on the GitHub contributors page.

nunit.analyzers's People

Contributors

mikkelbu avatar jasonbock avatar mgyongyosi avatar johanlarsson avatar 304notmodified avatar aolszowka avatar maximrouiller avatar

Watchers

James Cloos avatar  avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.