Oct 20, 2019 There are several unit test options available in Visual Studio 2019, but the decision to use MSTest for a particular project was out of my hands, so this post covers getting MSTest unit tests working on Visual Studio 2019 for Mac. The first step is to make sure you are using Visual Studio, and not confusing it with Visual Studio Code. Running the Unit Tests using the Visual Studio for Mac IDE. When the compilation of the unit tests is complete, you will be presented with the outcome of each of the test results, which can be filtered by which were Successful Tests, Failed Tests, or Ignored Tests within the Test Results pane, as shown in the following screenshot. CppUTest is a C /C based unit xUnit test framework for unit testing and for test-driving your code. It is written in C but is used in C and C projects and frequently used in embedded systems but it works for any C/C project. CppUTest’s core design principles are: Simple in design and simple in use. Portable to old and new platforms.

The Python extension supports testing with Python's built-in unittest framework as well as pytest. Nose is also supported, although the framework itself is in maintenance mode.

After enabling a test framework, use the Python: Discover Tests command to scan the project for tests according to the discovery patterns of the currently selected test framework. Once discovered, Visual Studio Code provides a variety of means to run tests and debug tests. VS Code displays test output in the Python Test Log panel, including errors caused when a test framework is not installed. With pytest, failed tests also appear in the Problems panel.

A little background on unit testing

(If you're already familiar with unit testing, you can skip to the walkthroughs.)

Visual Studio For Mac Unit Test Project

A unit is a specific piece of code to be tested, such as a function or a class. Unit tests are then other pieces of code that specifically exercise the code unit with a full range of different inputs, including boundary and edge cases.

For example, say you have a function to validate the format of an account number that a user enters in a web form:

Unit tests are concerned only with the unit's interface—its arguments and return values—not with its implementation (which is why no code is shown here in the function body; often you'd be using other well-tested libraries to help implement the function). In this example, the function accepts any string and returns true if that string contains a properly formatted account number, false otherwise.

To thoroughly test this function, you want to throw at it every conceivable input: valid strings, mistyped strings (off by one or two characters, or containing invalid characters), strings that are too short or too long, blank strings, null arguments, strings containing control characters (non-text codes), string containing HTML, strings containing injection attacks (such as SQL commands or JavaScript code), and so on. It's especially important to test security cases like injection attacks if the validated string is later used in database queries or displayed in the app's UI.

For each input, you then define the function's expected return value (or values). In this example, again, the function should return true for only properly formatted strings. (Whether the number itself is a real account is a different matter that would be handled elsewhere through a database query.)

With all the arguments and expected return values in hand, you now write the tests themselves, which are pieces of code that call the function with a particular input, then compare the actual return value with the expected return value (this comparison is called an assertion):

The exact structure of the code depends on the test framework you're using, and specific examples are provided later in this article. In any case, as you can see, each test is very simple: invoke the function with an argument and assert the expected return value.

The combined results of all the tests is your test report, which tells you whether the function (the unit), is behaving as expected across all test cases. That is, when a unit passes all of its tests, you can be confident that it's functioning properly. (The practice of test-driven development is where you actually write the tests first, then write the code to pass more and more tests until all of them pass.)

Visual Studio For Mac Unit Test Free

Because unit tests are small, isolated piece of code (in unit testing you avoid external dependencies and use mock data or otherwise simulated inputs), they're quick and inexpensive to run. This characteristic means that you can run unit tests early and often. Developers typically run unit tests even before committing code to a repository; gated check-in systems can also run unit tests before merging a commit. Many continuous integration systems also run unit tests after every build. Running the unit test early and often means that you quickly catch regressions, which are unexpected changes in the behavior of code that previously passed all its unit tests. Because the test failure can easily be traced to a particular code change, it's easy to find and remedy the cause of the failure, which is undoubtedly better than discovering a problem much later in the process!

For a general background on unit testing, see Unit Testing on Wikipedia. For a variety of useful unit test examples, see https://github.com/gwtw/py-sorting, a repository with tests for different sorting algorithms.

Example test walkthroughs

Python tests are Python classes that reside in separate files from the code being tested. Each test framework specifies the structure and naming of tests and test files. Once you write tests and enable a test framework, VS Code locates those tests and provides you with various commands to run and debug them.

For this section, create a folder and open it in VS Code. Then create a file named inc_dec.py with the following code to be tested:

With this code, you can experience working with tests in VS Code as described in the sections that follow.

Enable a test framework

Testing in Python is disabled by default. To enable testing, use the Python: Configure Tests command on the Command Palette. This command prompts you to select a test framework, the folder containing tests, and the pattern used to identify test files.

You can also configure testing manually by setting one and only one of the following settings to true: python.testing.unittestEnabled, python.testing.pytestEnabled, and python.testing.nosetestsEnabled. Each framework also has specific configuration settings as described under Test configuration settings for their folders and patterns.

It's important that you enable only a single test framework at a time. For this reason, when you enable one framework also be sure to disable the others. The Python: Configure Tests command does this automatically.

When you enable a test framework, VS Code prompts you to install the framework package if it's not already present in the currently activated environment:

Create tests

Each test framework has its own conventions for naming test files and structuring the tests within, as described in the following sections. Each case includes two test methods, one of which is intentionally set to fail for the purposes of demonstration.

Because Nose is in maintenance mode and not recommended for new projects, only unittest and pytest examples are shown in the sections that follow. (Nose2, the successor to Nose, is just unittest with plugins, and so it follows the unittest patterns shown here.)

Tests in unittest

Create a file named test_unittest.py that contains a test class with two test methods:

Tests in pytest

Create a file named test_pytest.py that contains two test methods:

Visual Studio C++ For Mac

Test discovery

VS Code uses the currently enabled testing framework to discover tests. You can trigger test discovery at any time using the Python: Discover Tests command.

python.testing.autoTestDiscoverOnSaveEnabled is set to true by default, meaning test discovery is performed automatically whenever you save a test file. To disable this feature, set the value to false.

Test discovery applies the discovery patterns for the current framework (which can be customized using the Test configuration settings). The default behavior is as follows:

python.testing.unittestArgs: Looks for any Python (.py) file with 'test' in the name in the top-level project folder. All test files m