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Updated filtering settings in ReSharper 2021.1

Visual Studio Integration to OpenCover Resources. MIT License Releases 2. V0.8.1 Latest Aug 13, 2015 + 1 release Packages 0. No packages published. Launch the Developer Command Prompt for Visual Studio: In the Windows Start menu, choose Visual Studio 2017 Developer Command Prompt for VS 2017. In the Windows Start menu, choose Visual Studio 2019 Developer Command Prompt for VS 2019. The best solution I found for code coverage in Visual Studio is as follows: Open Visual Studio 2017; Go to Tools-Extensions and Updates; Go to 'Online', in the search (top right of the window) type 'AxoCover' Click install, follow the instructions (i.e. Close Visual Studio to start the install) Reopen Visual Studio; Build your project. Nice and free.Net code coverage support for Visual Studio with OpenCover. Features: Run, debug and cover unit tests in.Net Framework projects (.Net Core / Xamarin projects are however NOT supported) Browse unit tests in a clean hierarchical view; Display sequence and branch coverage in the code editor with detailed display for partially. OpenCover/opencover A code coverage tool for.NET 2 and above (WINDOWS OS only), support for 32 and 64 processes with both branch and sequence points.: 16.5.0-preview-20191115-01 74,697: 16.4.0. Vstest visual-studio unittest testplatform mstest microsoft test testing codecoverage code-coverage.

You can now configure coverage filters in Visual Studio with ReSharper in the same way as in JetBrains Rider.

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dotCover console runner as a .NET global tool 2021.1

You can install the dotCover console runner as a dotnet global tool: dotnet tool install --global JetBrains.dotCover.GlobalTools --version 2021.1.0

Use it to analyze coverage in your .NET Core projects and unit tests.

.NET 5 support 2020.3

dotCover lets you perform coverage analysis of applications targeting .NET 5.

Improved Continuous Testing 2020.3

Now, you can refine what projects should be built when a continuous testing session is triggered. This lets you speed up continuous testing sessions. The feature works in both Visual Studio and Rider.

Improved support for projects targeting multiple frameworks 2020.3

Now, the Unit Tests Coverage window in both Visual Studio and Rider lets you filter coverage results by target framework.

Methods with outdated coverage info are shown in the coverage tree 2020.3

Before 2020.3, methods with outdated coverage info were highlighted only with a marker in the gutter. Now, in Visual Studio, methods with outdated coverage info are also highlighted in the Unit Tests Coverage window.

Code coverage in solution highlight level options 2020.3

Now, you can use the Rider’s solution highlight level options to turn code coverage highlighting on and off.

Support for ARM64 architecture 2020.2

The dotCover console runner for Linux (Debian and Ubuntu) lets you perform coverage analysis on ARM64 systems.

Improvements in dotCover for Rider 2020.2

The new release brings several useful new features to the dotCover plugin in Rider:

• The Unit Tests Coverage window now lets you export coverage reports.
• The Unit Tests Coverage window now has a quick search feature.
• You can now navigate from a character in the code to that character in the coverage tree (Navigate to | Coverage Tree).

Improvements in the console runner 2020.2

• Summary report type. The --reportype=SummaryXml parameter lets you create a new report type which includes data on how many classes, methods, and statements are covered in total.
• Now the runner can generate reports for several types at once.

Coverage filters in JetBrains Rider 2020.1

Now, you can apply both runtime and coverage results filters in JetBrains Rider.

Support for Unity on Windows, macOS, and Linux 2019.3

dotCover 2019.3 provides support for Unity 2018.3 and later on all operating systems. To perform coverage analysis of Unity tests, you must use JetBrains Rider.

Various improvements 2019.3

dotCover 2019.3 gets a couple more improvements:

• Support for Microsoft Fakes.
• Ability to group coverage results by nested namespaces in Rider, Visual Studio, and in reports generated by the dotCover console tool.

Support for Mono on Windows, macOS, and Linux 2019.2

dotCover 2019.2 gets support for Mono 5.10 and later on Windows, macOS, and Linux. To perform coverage analysis on these operating systems, you must use JetBrains Rider or dotCover console runner.

Console runner on Linux and macOS 2019.2

Now, you can use dotCover console runner not only on Windows but on macOS and Linux as well. To be more convenient on new platforms, the tool accepts command arguments in Unix-style syntax. For example, now, both /TargetExecutable=MyApp.exe and -⁠-⁠targetexecutable:MyApp.exe are valid.

In addition, the console runner gets two new commands to simplify coverage analysis of .NET Core and Mono applications and unit tests: cover-dotnet and cover-mono.

Support for .NET Core on macOS and Linux 2019.1

dotCover 2019.1 provides full support for .NET Core 2.0 – 3.0 projects not only on Windows, but on macOS and Linux as well. To perform coverage analysis on these operating systems, you must use JetBrains Rider or dotCover console runner.

Code coverage is one of the most common metrics that helps software developers and quality assurance (QA) specialists assess testing efficiency. Evaluating code coverage helps you determine testing completeness, identify unnecessary code, and detect bugs.

In this article, we briefly explore what code coverage is, list the most popular tools for measuring it, and show how to evaluate the percentage of code coverage using the open-source OpenCover tool. This article will be helpful for developers, testers, and QA specialists who want to learn how to use the OpenCover tool for code coverage measurement.

Contents:

What is code coverage?

Code coverage is a measure of the percentage of program source code covered with unit tests. It can vary from 0% to 100%.

While test coverage measures the number of performed tests and monitors the quality of testing, code coverage indicates the percentage of lines of code covered with unit tests.

Why do you need to measure code coverage?

Evaluating code coverage is an essential part of test-driven development, which is based on writing tests before writing code. The idea is to reduce the amount of unnecessary code and avoid bugs.

Read also:
Test-Driven Development vs Behavior-Driven Development: What is the Difference?

One of the crucial benefits of analyzing code coverage is that it gives developers an understanding of whether there are enough tests in the unit test suite or whether they should create more tests to cover as many lines of code as possible.

Let’s take a look at the opportunities code coverage evaluation provides to developers:

• Check the completeness of unit tests. If code coverage results aren’t satisfying and the percentage of non-covered code is too high, you can add more unit tests to improve the score.
• Prevent defects at early project stages. When analyzing code coverage results, you can find areas of the program that were left unattended during testing. Such code can potentially be dead or useless — or contain bugs.
• Adjust tests to check various sets of input parameters. Passing tests shows that correct performance is possible, but only for a certain set of input parameters. If you change a method, the test can fail. After that, your code coverage score can change and direct your attention to a potential bug or another issue.
• Access a wide selection of tools. You can find commercial and open-source tools to automate the measuring process.

Despite its advantages, measuring code coverage isn’t a silver bullet for checking code efficiency. One of the biggest challenges related to code coverage is interpreting the results.

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Complete code coverage doesn’t mean that a project is fully tested. Even an empty method can be 100% covered, but this code is useless. 80% coverage is also a decent result, but there’s a risk that this 80% won’t cover the main scenario.

It’s essential to remember that code coverage results can’t point to flaws in the logic of your code. They also won’t report on the absence of features that should have been implemented.

Another crucial thing is to understand when you should concentrate on a high coverage percentage and when you shouldn’t.

It’s easier to cover more code with unit tests in small and mid-sized projects — and when using test automation. And it’s almost impossible to achieve high code coverage in a large-scale project with complicated logic that can lead to multiple errors. Google has spent more than a decade attempting to refine their coverage infrastructure and validate the most suitable approach.

Covering code with tests can be time-consuming and, therefore, costly. It’s reasonable to do so when you’re already familiar with the entire codebase, have information about recent and planned changes, and know specific requirements. However, if code is old and has gone through multiple changes — and if you don’t have clear requirements on how it should perform — covering it with tests will take lots of time and most likely won’t be very helpful.

Related services

Specialized Quality Assurance & Testing Solutions

Choosing a tool for checking code coverage

Covering code with unit tests is as time-consuming as creating code. That’s why there’s a wide range of tools, both paid and free, that can accelerate the evaluation of code coverage.

Most tools for measuring code coverage are commercial, like NCover, dotCover, NCrunch, and NDepend. Also, Microsoft Visual Studio is equipped with a convenient code coverage tool with an attractive and intuitive user interface. However, it’s only available in the paid Visual Studio Enterprise version.

On the bright side, there are a fair number of efficient open-source code coverage tools. The most popular are:

• OpenCover — Works on the .NET platforms starting from 2.0
• AxoCover — An extension for Visual Studio that works with OpenCover (not available for Visual Studio versions older than 2017)
• Cobertura — A free tool that calculates the percentage of code executed during tests and identifies which parts of your Java program are lacking test coverage
• JaCoCo — An open-source toolkit for measuring and reporting Java code coverage, distributed under the terms of the Eclipse Public License
• Coverage.py — A tool for measuring code coverage for Python programs

In our projects, we mostly use OpenCover, which has proved its efficiency over the years and has numerous benefits:

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• Doesn’t depend on Visual Studio versions
• Works on .NET platforms starting from 2.0
• Provides a detailed report on code coverage results
• Setup is quick and only requires installing a pair of NuGet packages
• Provides flexible parameter settings
• Can be easily launched from a single configuration file without the need for configuring settings each time you run tests
• Supports both 32-bit and 64-bit processors

Now, let’s explore how to evaluate code coverage with the OpenCover tool.

Read also:
How to Detect Vulnerabilities in Software When No Source Code Is Available

Measuring code coverage with OpenCover

To show you an example of how to measure code coverage, we’ll use the following tools:

• OpenCover
• ReportGenerator
• Visual Studio
• NuGet
• VSTest.Console.exe

Since OpenCover doesn’t have a graphical interface, it can be launched and run only in the console. Its source code can be found on the official GitHub page.

To simplify work with OpenCover results, we usually use the free ReportGenerator tool. ReportGenerator converts code coverage results into readable files in various formats like HTML, Cobertura, and CSV. This tool works with reports created by OpenCover, dotCover, Visual Studio, NCover, Cobertura, JaCoCo, Clover, Gcov, and Lcov.

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ReportGenerator will convert an XML file with code coverage results generated by OpenCover into a readable HTML report.

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Configuring the tools

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Both tools are available for installation through NuGet at the following links: OpenCover and ReportGenerator. We can easily install the required packages in Visual Studio using the NuGet package manager.

Once both tools are installed, we can directly interact with them from the console. They can be called with the following commands: