Unit testing robot code

pyfrc comes with robot.py extensions that support testing robot code using the py.test python testing tool. To run the unit tests for your robot, just run your robot.py with the following arguments:

Windows:   py -3 robot.py test

Linux/macOS: python3 robot.py test

Your tests must be in a directory called ‘tests’ either next to robot.py, or in the directory above where robot.py resides. See ‘samples/simple’ for an example test program that starts the robot code and runs it through autonomous mode and operator mode.

Builtin unit tests

pyfrc comes with testing functions that can be used to test basic functionality of just about any robot, including running through a simulated practice match. As of pyfrc 2016.1.1, to add these standardized tests to your robot code, you can run the following:

Windows:   py -3 robot.py add-tests

Linux/macOS: python3 robot.py add-tests

Running this command creates a directory called ‘tests’ if it doesn’t already exist, and then creates a file in your tests directory called pyfrc_test.py, and put the following contents in the file:

from pyfrc.tests import *

Unlike previous years, all tests work on all types of robots now.

As of pyfrc 2015.0.2, the --builtin option allows you to run the builtin tests without needing to create a tests directory.

Writing your own test functions

Often it’s useful to create custom tests to test specific things that the generic tests aren’t able to test. When running a test, py.test will look for functions in your test modules that start with ‘test_’. Each of these functions will be ran, and if any errors occur the tests will fails. A simple test function might look like this:

def two_plus(arg):
    return 2 + arg

def test_addition():
    assert two_plus(2) == 4

The assert keyword can be used to test whether something is True or False, and if the condition is False, the test will fail.

Pytest supports something called a ‘fixture’, which allows you to add an argument to your test function and it will call the fixture and pass the result to your test function as that argument. pyfrc has a custom pytest plugin that it uses to provide this special functionality to your tests.

For more information:

code coverage for tests

pyfrc supports measuring code coverage using the coverage.py module. This feature can be used with any robot.py commands and provide coverage information.

For example, to run the ‘test’ command to run unit tests:

Windows:   py -3 robot.py coverage test

Linux/macOS: python3 robot.py coverage test

Or to run coverage over the simulator:

Windows:   py -3 robot.py coverage sim

Linux/macOS: python3 robot.py coverage sim

Running code coverage while the simulator is running is nice, because you don’t have to write unit tests to make sure that you’ve completely covered your code. Of course, you should write unit tests anyways… but this is good for developing code that needs to be run on the robot quickly and you need to make sure that you tested everything first.

When using the code coverage feature, what actually happens is robot.py gets executed again, except this time it is executed using the coverage module. This allows coverage.py to completely track code coverage, otherwise any modules that are imported by robot.py (and much of robot.py itself) would not be reported as covered.


There is a py.test module called pytest-cov that is supposed to allow you to run code coverage tests. However, I’ve found that it doesn’t work particularly well for me, and doesn’t appear to be maintained anymore.


For some reason, when running the simulation under the code coverage tool, the output is buffered until the process exits. This does not happen under py.test, however. It’s not clear why this occurs.

Next Steps

Learn more about some Best Practices when creating robot code.