C++ tests are located in /tests and Python (binding) tests in /python/test. See the documentation on building for the C++ tests and /python/test/readme.md for the latter.

What to test?#

Adding a feature should be accompanied by tests to ensure its functionality is sound. That means 1. identify the core ideas of your feature 2. find the functions/classes that encapsulate these ideas 3. for each add a test case that covers it

The core motivation is to capture the essence of your feature and to protect it against accidental change. This is what enables us to freely add optimisations, and perform refactoring.


This example might touch parts of Arbor that you are unfamiliar with. Don’t Panic! The details are less important than the general approach to adding tests. Imagine adding a new feature that is intended to improve performance during the communication step. Spikes should be transferred pre-sorted to avoid locally sorting them after the exchange. Also assume, just for the sake of this example, that you decided to add your own radix sort algorithm, since you expect it to be faster for this particular usecase.

Thus, the tests added should be

  1. sorting algorithm

  • the application of sort sorts the given array. This seems trivial, but is really the core of what you are doing!

  • corner cases like: empty array, all elements equal, … are treated greacefully

  • if the sort is intended to be stable, check that equal elements do not switch order

  1. local sorting

  • spikes are – after sorting – ordered by their source id and in case of ties by time

  • corner cases: NaN, negative numbers, …

  1. global sorting

  • after MPI exchange, each sub-array is still sorted

  • by the guarantees of MPI_Allgather, the global array is sorted

Note that we added tests that are only applicable when eg MPI is enabled. Our test runners probe the different combinations automatically, see below.

Next, we would ask you to prove that this change does as promised, ie it improves performance. When adding a new user-facing feature, also consider adding an example showing off your cool new addition to Arbor.

Regression tests#

However, it’s impossible to foresee every dark corner of your code. Inevitably, bugs will occur. When fixing a bug, please add a test case that covers this particular sequence of events to catch this bug in the future (imagine someone inadvertently removing your fix).

C++ tests#

We are using the GTest library for writing tests. Each group of tests should be contained in a .cpp file in test/unit (do not forget to add it to the CMakeLists.txt!). To get access to the library and a battery of helpers include common.hpp. Test cases are defined vi the TEST macro which takes two arguments group and case. Inside cases macros like ASSERT_TRUE can be used. Another helpful feature is that the test executable accepts arguments on the commandline. Of these we would like to point out

  • --gtest_catch_exceptions allows for disabling exception catching by the framework. Handy when running the tests in a debugger.

  • --gtest_throw_on_failure turns missed assert into exceptions, likewise useful in a debugger

  • --gtest_filter to filter the tests to run. Can cut down the runtrip time when working on a specific feature.

For more information on GTest refer to the documentation <https://google.github.io/googletest/>`_ and our existing tests.

Python tests#

The Python tests uses the unittest and its test discovery mechanism. For tests to be discovered they must meet the following criteria:

  • Located in an importable code folder starting from the python/test root. If you introduce subfolders they must all contain a __init__.py file.

  • The filenames must start with test_.

  • The test case classes must begin with Test.

  • The test functions inside the cases must begin with test_.

To run the tests locally use python -m unittest from the python directory.


Multiple tests may require the same reusable piece of test setup to run. You can speed up the test writing process for everyone by writing these reusable pieces as a fixture. A fixture is a decorator that injects the reusable piece into the test function. Fixtures, and helpers to write them, are available in python/test/fixtures.py. The following example shows you how to create a fixture that returns the arbor version, and optionally the path to it:

import arbor

# This decorator converts your function into a fixture decorator.
def arbor_info(return_path=False):
  if return_path:
    return (arbor.__version__, arbor.__path__)
    return (arbor.__version__,)

Whenever you are writing a test you can now apply your fixture by calling it with the required parameters, and adding a parameter to your function with the same name as the fixture:

# Import fixtures.py
from .. import fixtures

def test_up_to_date(arbor_info):

Feature dependent tests#

Certain tests need to be guarded by feature flags, notably ARB_MPI_ENABLED and ARB_GPU_ENABLED. Another important (especially when dealing with mechanisms, modcc, and the ABI) but less obvious feature is SIMD. The combinations arising from the cartesian product of OS=Linux|MacOS x SIMD=ON|OFF x MPI=ON|OFF is tested automatically on GitHub CI. As no instances with GPUs are provided, GPU features are tested via CSCS’ GitLab. Such a run is initiated by commenting bors try in the PR discussion.