Testing Highcharts Stock for Python 

Testing Philosophy 

Note

Unit tests for Highcharts for Python are written using pytest [1] and a comprehensive set of test automation are provided by tox [2].

There are many schools of thought when it comes to test design. When building Highcharts for Python, we decided to focus on practicality. That means:

DRY is good, KISS is better. To avoid repetition, our test suite makes extensive use of fixtures, parametrization, and decorator-driven behavior. This minimizes the number of test functions that are nearly-identical. However, there are certain elements of code that are repeated in almost all test functions, as doing so will make future readability and maintenance of the test suite easier.

Coverage matters…kind of. We have documented the primary intended behavior of every function in the Highcharts for Python library, and the most-likely failure modes that can be expected. At the time of writing, we have about 94% code coverage. Yes, yes: We know that is less than 100%. But there are edge cases which are almost impossible to bring about, based on confluences of factors in the wide world. Our goal is to test the key functionality, and as bugs are uncovered to add to the test functions as necessary.

Test Organization 

Each individual test module (e.g. test_chart.py) corresponds to a conceptual grouping of functionality. For example:

tests/test_chart.py tests the contents of the highcharts_core.chart module.
tests/options/annotations/test_annotations.py tests the contents of highcharts_core.options/annotations/__init__.py.

Because Highcharts for Python largely depends on designing classes that serialize and deserialize content to/from JavaScript, we have implemented a standard number of tests as Pytest fixtures. These tests are then configured using a set of parameters which configure their standard inputs and expected outputs, as applicable.

If you are implementing a new class, please use the same test structure as used for the other test modules. If you are adding a new method that is specific to one class, you do not need to use this pattern. However, if it is a method that will be inherited by or used by other classes, then we ask that you implement a similar pattern to keep test maintenance as easy as possible.

Configuring & Running Tests 

Installing with the Test Suite 

$ pip install highcharts-stock[tests]

Command-line Options 

Highcharts for Python does not use any custom command-line options in its test suite.

Tip

For a full list of the CLI options, including the defaults available, try:

highcharts-stock $ cd tests/
highcharts-stock/tests/ $ pytest --help

Configuration File 

Because Highcharts for Python has a very simple test suite, we have not prepared a pytest.ini configuration file.

Running Tests 

tests/ $ pytest

tests/ $ pytest tests/test_module.py

tests/ $ pytest tests/test_module.py -k 'test_my_test_function'

Skipping Tests 

Note

Because of the simplicity of Highcharts for Python, the test suite does not currently support any test skipping.

Incremental Tests 

Note

The Highcharts for Python test suite does support incremental testing using, however at the moment none of the tests designed rely on this functionality.

A variety of test functions are designed to test related functionality. As a result, they are designed to execute incrementally. In order to execute tests incrementally, they need to be defined as methods within a class that you decorate with the @pytest.mark.incremental decorator as shown below:

@pytest.mark.incremental
class TestIncremental(object):
    def test_function1(self):
        pass
    def test_modification(self):
        assert 0
    def test_modification2(self):
        pass

This class will execute the TestIncremental.test_function1() test, execute and fail on the TestIncremental.test_modification() test, and automatically fail TestIncremental.test_modification2() because of the .test_modification() failure.

To pass state between incremental tests, add a state argument to their method definitions. For example:

@pytest.mark.incremental
class TestIncremental(object):
    def test_function(self, state):
        state.is_logged_in = True
        assert state.is_logged_in = True
    def test_modification1(self, state):
        assert state.is_logged_in is True
        state.is_logged_in = False
        assert state.is_logged_in is False
    def test_modification2(self, state):
        assert state.is_logged_in is True

Given the example above, the third test (test_modification2) will fail because test_modification updated the value of state.is_logged_in.

Note

state is instantiated at the level of the entire test session (one run of the test suite). As a result, it can be affected by tests in other test modules.