Testing

Pytest

Pytest is a testing framework for Python. It is a mature full-featured Python testing tool.

Doctest

Python supports doctest, which is very convenient for testing small code snippets within docstrings. And, it's make the usage of the code more clear.

def add(a, b):
    """
    >>> add(1, 2)
    3
    >>> add(2, 3)
    5
    """
    return a + b

Run doctest

Run doctest for a specific moduleRun doctest for all the modulesRun with pytest

python -m doctest -v mppt/hello.py

python -m doctest -v mppt/*.py

pytest --doctest-modules

Hypothesis

Hypothesis is a library for property based testing.

We can print the statistics of the hypothesis testing by adding the following command line option:

RUN: pytest --hypothesis-show-statistics tests/test_sort.py

Testing statistics

======================== Hypothesis Statistics =========================
tests/test_sort.py::TestQuickSort::test_integer:

  - during reuse phase (0.00 seconds):
    - Typical runtimes: < 1ms, of which < 1ms in data generation
    - 2 passing examples, 0 failing examples, 0 invalid examples

  - during generate phase (0.08 seconds):
    - Typical runtimes: < 1ms, of which < 1ms in data generation
    - 98 passing examples, 0 failing examples, 2 invalid examples

  - Stopped because settings.max_examples=100


tests/test_sort.py::TestQuickSort::test_float:

  - during reuse phase (0.00 seconds):
    - Typical runtimes: < 1ms, of which < 1ms in data generation
    - 2 passing examples, 0 failing examples, 0 invalid examples

  - during generate phase (0.05 seconds):
    - Typical runtimes: < 1ms, of which < 1ms in data generation
    - 98 passing examples, 0 failing examples, 11 invalid examples

  - Stopped because settings.max_examples=100


tests/test_sort.py::TestQuickSort::test_string:

  - during reuse phase (0.00 seconds):
    - Typical runtimes: < 1ms, of which < 1ms in data generation
    - 3 passing examples, 0 failing examples, 0 invalid examples

  - during generate phase (0.40 seconds):
    - Typical runtimes: ~ 0-2 ms, of which ~ 0-2 ms in data generation
    - 297 passing examples, 0 failing examples, 4 invalid examples

  - Stopped because settings.max_examples=300

Locust

Locust is an easy-to-use, distributed, user load testing(stress testing) tool.

Codecov

Codecov is a code coverage tool.

Some configurations are needed to use Codecov to generate test coverage report.

Set the codecov token CODECOV_TOKEN

• Login your codecov account with GitHub: https://about.codecov.io/
• In codecov account, select the repository and you'll see a page named Let's get your repo covered. Just COPY the token here.
• In the GitHub repository: Setting -> Secrets and variables: Add a Repository secrets named CODECOV_TOKEN and set the value with the token