Getting Started Guide to Pytest Framework: Basic Knowledge, Common Functions and Practical Suggestions

Pytest Framework Getting Started Guide: Basic Knowledge, Common Functions and Practical Suggestions

Abstract: This article will introduce the basic knowledge of the Pytest framework, including installation and configuration, and test functions. Basic structures, concepts such as assertions and exception handling. At the same time, we will also introduce common functions in the Pytest framework, such as parameterization, fixtures, and marks, as well as some practical suggestions. By studying this article, readers will be able to have a comprehensive and in-depth understanding of the Pytest framework and be able to flexibly use it for automated testing.

1. Installation and configuration of Pytest framework
Pytest framework is a powerful automated testing framework written in Python, which can be installed using the pip command. After installation, you need to perform some basic configuration, such as defining the naming convention of files and folders where test cases are stored, and configuring options when running tests, such as the output format of test reports, etc.

2. Basic knowledge

1. Basic structure of test function
   In the Pytest framework, each test case is represented by a Python function. Test functions must start with "test_" and can contain assertions for judgment. Examples are as follows:

def test_add(): assert add(2, 3) == 5

2. Assertion and exception handling
   Assertion is a technique commonly used in automated testing to determine the difference between expected results and actual results. The Pytest framework provides a wealth of assertion methods, such as assert, assertEqual, assertTrue, etc. In addition, Pytest also supports catching and handling exceptions, such as try-except statement blocks. Examples are as follows:

def test_divide(): try: assert divide(4, 2) == 2 except ZeroDivisionError: pytest.fail("除数不能为0")

3. Common functions

1. Parameterization
   Parameterization is an important function of the Pytest framework, which can make a test case run multiple times. Use different input data each time. By using the @pytest.mark.parametrize decorator, we can pass different combinations of parameters to the test function. An example is as follows:

@pytest.mark.parametrize("x, y, result", [(1, 2, 3), (4, 5, 9)]) def test_add(x, y, result): assert add(x, y) == result

2. Fixture (fixture)
   Fixture is another core function in the Pytest framework, used for some preparation and preparation before, during or after the test case execution. Clean up. You can use the @pytest.fixture decorator to define fixture functions and reference the fixture in the test function. An example is as follows:

@pytest.fixture def setup(): # 夹具的准备工作 yield # 夹具的清理工作 def test_function(setup): # 测试函数的执行代码

3. Marker
   Marker is a feature in the Pytest framework that is used to classify test cases. For example, we can add tags to test cases such as "smoke" (smoke test), "functional" (functional test), etc. Afterwards, we can execute specific test cases based on the tags. Examples are as follows:

@pytest.mark.smoke def test_login(): # 登录测试的代码

4. Practical suggestions

1. Write concise and easy-to-read test case code. Use descriptive function and variable names, and clear comments to increase readability.
2. Use fixtures to manage the pre- and post-conditions of test cases to ensure the independence and repeatability of test cases.
3. Use parameterization to test a large number of test cases with similar functionality to save time writing duplicate code.
4. Use command line options to control how tests are run, for example pytest -m "smoke" only executes test cases marked "smoke".
5. Use plug-ins and extensions to enhance the functionality of the testing framework, such as generating test reports, customizing test rules, etc.

Conclusion: This article introduces the basic knowledge, common functions and some practical suggestions of the Pytest framework. By studying this article, readers will be able to skillfully use the Pytest framework for automated testing, and be able to write and manage test case code more efficiently.

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