A multi-language overview on how to test your research project code
Code testing is essential to identify and fix potential issues, to maintain sanity over the course of the development of the project and quickly identify bugs, and to ensure the reliability and sanity of your experiment overtime.
This post is part of a series of posts on best practices for managing research project code. Much of this material was developed in collaboration with Mauro Werder as part of the Course On Reproducible Research, Data Pipelines, and Scientific Computing (CORDS). If you have experiences to share or spot any errors, please reach out!
Content
Unit testing
Unit testing involves testing a unit of code, typically a single function, to ensure its correctness. Here are some key aspects to consider:
- Test for correctness with typical inputs.
- Test edge cases.
- Test for errors with bad inputs.
Some developers start writing unit tests before writing the actual function, a practice known as Test-Driven Development (TDD). Define upstream on a piece of paper the behavior of the function, write corresponding tests, and when all tests pass, you are done. This philosophy ensures that you have a well-tested implementation, and avoids unnecessary feature development, forcing you to focus only on what is needed. While TDD is a powerful idea, it can be challenging to follow strictly.
A good idea is to write an additional test when you find a bug in your code.
Lightweight formal tests with assert
The simplest form of unit testing involves some sort of assert statement.
Python
def fib(x):
if x <= 2:
return 1
else:
return fib(x - 1) + fib(x - 2)
assert fib(0) == 0
assert fib(1) == 1
assert fib(2) == 1
Julia
@assert 1 == 0
When one test is broken, you’ll get an error for the corresponding test, which you’ll need to fix to check the following tests.
In Julia or Python, you could directly place the assert statement after your functions. This way, tests are run each time you execute the script. Here is nother pythonic approach, which can be used to decouple the test
def fib(x):
if x <= 2:
return 1
else:
return fib(x - 1) + fib(x - 2)
if __name__ == '__main__':
assert fib(0) == 0
assert fib(1) == 1
assert fib(2) == 1
assert fib(6) == 8
assert fib(40) == 102334155
print("Tests passed")
Consider using np.isclose, np.testing.assert_allclose (Python) or approx (Julia) for floating point comparisons.
Testing with a test suite
Once you have many tests, it makes sense to group them into a test suite and run them with a test runner. This approach will run all tests, even though some are broken, and retrieve and informative statements on those tests that passed, and those that did not. As you’ll see, it also allows to automatically run the test at each commit, with continuous integration.
Python
Two main frameworks for unit tests in Python are pytest and unittest, with pytest being more popular.
Example using pytest:
from src.fib import fib
import pytest
def test_typical():
assert fib(1) == 1
assert fib(2) == 1
assert fib(6) == 8
assert fib(40) == 102334155
def test_edge_case():
assert fib(0) == 0
def test_raises():
with pytest.raises(NotImplementedError):
fib(-1)
with pytest.raises(NotImplementedError):
fib(1.5)
Run the tests with:
pytest test_fib.py
Julia
Built in module Test, relying on the macro @test. Consider grouping your tests with
julia> @testset "trigonometric identities" begin
θ = 2/3*π
@test sin(-θ) ≈ -sin(θ)
@test cos(-θ) ≈ cos(θ)
@test sin(2θ) ≈ 2*sin(θ)*cos(θ)
@test cos(2θ) ≈ cos(θ)^2 - sin(θ)^2
end;
This will nicely output
Test Summary: | Pass Total Time
trigonometric identities | 4 4 0.2s
which comes handy for grouping tests applied to a single function or concept. Test functions may require additional packages to your minimum working environment specified at your package root folder. An additional virtual environment may be specified for tests! To develop my tests interactively, I like using TestEnv. Unfortunately, using Pkg.activate in tests would not work there, you. You need TestEnv to have access to your package functions;
In your package environment,
using TestEnv
TestEnv.activate()
will activate the test environment.
To reactivate the normal environment,
Pkg.activate(".")
Here is a nice thread to read more on that.
R
Testing non-pure functions and classes
For nondeterministic functions, provide the random seed or variables needed by the function as arguments to make them deterministic. For stateful functions, test postconditions to ensure the internal state changes as expected. For functions with I/O side effects, create mock files to verify proper input reading and expected output.
Python
def file_to_upper(in_file, out_file):
fout = open(out_file, 'w')
with open(in_file, 'r') as f:
for line in f:
fout.write(line.upper())
fout.close()
import tempfile
import os
def test_file_to_upper():
in_file = tempfile.NamedTemporaryFile(delete=False, mode='w')
out_file = tempfile.NamedTemporaryFile(delete=False)
out_file.close()
in_file.write("test123\nthetest")
in_file.close()
file_to_upper(in_file.name, out_file.name)
with open(out_file.name, 'r') as f:
data = f.read()
assert data == "TEST123\nTHETEST"
os.unlink(in_file.name)
os.unlink(out_file.name)
Continuous integration
Automated testing on local machines is useful, but you can do better with continuous integration (CI). In fact, CI is essential for projects involving multiple developers and various target platforms. CI consists in running tests whenever changes are committed.
CI can also be used to automatically build documentation, check for code coverage, and more. GitHub Actions is a popular CI tool available within GitHub.
CI is based on .yaml files, which specify the environment to run the script. You can build matrices to test across different environments (e.g. Linux, Windows and MacOS, with different versino of python or Julia). Jobs will be created that run our tests for each permutation of these.
An example CI.yaml file for Julia
name: Run tests
on:
push:
branches:
- master
- main
pull_request:
permissions:
actions: write
contents: read
jobs:
test:
runs-on: ${{ matrix.os }}
strategy:
matrix:
julia-version: [‘1.6’, ‘1’, ’nightly’]
julia-arch: [x64, x86]
os: [ubuntu-latest, windows-latest, macOS-latest]
exclude:
- os: macOS-latest
julia-arch: x86
steps:
- uses: actions/checkout@v4
- uses: julia-actions/setup-julia@v1
with:
version: ${{ matrix.julia-version }}
arch: ${{ matrix.julia-arch }}
- uses: julia-actions/cache@v1
- uses: julia-actions/julia-buildpkg@v1
- uses: julia-actions/julia-runtest@v1
An example CI.yaml file for Python
This action installs the conda environment called glacier-mass-balance, specified in the environment.yml file.
It then runs pytest, supposing that you have a test/ folder where your functions are located. First try whether pytest works locally. Do not forget to have pytest in your dependencies.
name: Run tests
on: push
jobs:
miniconda:
name: Miniconda ${{ matrix.os }}
runs-on: ${{ matrix.os }}
strategy:
matrix:
os: ["ubuntu-latest"]
steps:
- uses: actions/checkout@v2
- uses: conda-incubator/setup-miniconda@v2
with:
environment-file: environment.yml
activate-environment: glacier-mass-balance
auto-activate-base: false
- name: Run pytest
shell: bash -l {0}
run: |
pytest
Cool tip
You can include a cool badge to show visually whether your tests are passing or failing, like so
You can get the code for this badge by going on your github repo, then Actions. Click on the test action, then on top right click on the ... and `Create status badge```.
Cool right?
Other types of tests
- Docstring tests: Unit tests embedded in docstrings.
- Integration tests: Test whether multiple functions work correctly together.
- Regression tests: Ensure your code produces the same outputs as previous versions.
Resources
- Official GitHub documentation on building and testing Python projects
- CI with GitHub Action and Docker for Python
- Julia documentation on unit testing
- Good Research Practices: Testing
- The Carpentries: Python Testing
Take-home messages
- Systematically implementing testing allows you to ensure the sanity of your code
- The overhead cost of testing is usually well balanced by the reduced time spent downstream in identifying bugs
Victor Boussange
Postdoctoral researcher
Researcher in ecology and evolution, scientific machine learning enthusiastic.