How to calculate the code coverage rate

How to use the COVERAGE library in Python to calculate the code coverage Overview: Code coverage is one of the important indicators of testing software quality and performance.The COVERAGE library is a Python library for calculating code coverage.It can tell you which parts of the code during the test have been running and which parts have not been tested.In this article, we will introduce how to use the COVERAGE library to calculate the code coverage and display the related programming code and configuration. Install the coverage library: Before starting, you need to make sure that the Coverage library has been installed.You can use the following command to install the COVERAGE library in the command line: pip install coverage Calculate code coverage: Below is a simple example, demonstrating how to use the COVERAGE library to calculate the code coverage. Suppose we have a Python program called `Calculator.py`, which contains a basic calculator class, as well as some methods to perform additional, subtraction, multiplication, and dividend operations.We want to test these methods and calculate the code coverage. First, create a test file called `test_calculator.py`, which contains test cases for each method for` Calculator.py`.In each test case, use the `Start () and` STOP () `function of the` COVERAGE` Module to start and stop the tracking of code coverage.Finally, use the `Report ()` function to generate code coverage report. test_calculator.py: python import coverage cov = coverage.Coverage() cov.start() # Import module to be tested import calculator # 测试 测试 测试 测试 def test_addition(): assert calculator.add(2, 3) == 5 #The test subtraction def test_subtraction(): assert calculator.subtract(5, 3) == 2 # 测试 法 法 def test_multiplication(): assert calculator.multiply(4, 3) == 12 # 测试 测试 def test_division(): assert calculator.divide(10, 2) == 5 # Stop code coverage tracking and generate report cov.stop() cov.save() cov.report() After executing the `test_calculator.py` in the command line, the Coverage library will start tracking the code coverage and generate a simple report after the test case is executed.The coverage of each method will be displayed in the report, including the row of the tested coverage and the line that is not tested. Related configuration: In addition to the basic configuration above, Coverage also provides some configuration options to better meet your needs.Here are some commonly used configuration options: 1. `omit`: Used to specify the file or directory list to be ignored. The code in these files or directory will not be included in the coverage statistics. 2. `Include`: It is used to specify the file or directory list that includes in the coverage statistics. 3. `Exclude`: The file or directory list used to specify the file or directory list from the coverage statistics. 4. `Branch`: The calculation of enabled branch coverage, that is, determine whether the different branches of the code during the execution process have been accessed. 5. Source`: It is used to specify the source code directory to track the code coverage. For example, if you want to specify the files/directory to be included and eliminated, you can use the `Include` and` OMIT` parameters in the initialization code of the `COV` object: python cov = coverage.Coverage(include=['project/*'], omit=['project/tests/*']) This will include all the files in the `Project` directory, but it will exclude the files in the` Project/Tests` directory. in conclusion: By using the COVERAGE library, you can easily calculate the code coverage and generate corresponding reports.By testing the coverage of the code, you can better understand the quality and performance of the code and find out the part that has not been tested in time.I hope this article will help to calculate the code coverage using the Coverage library!