Automation of Testing
Explore how to automate testing in machine learning projects using code coverage and continuous integration. Understand methods to measure tested code sections, improve test efficiency, and integrate testing into CI/CD pipelines for consistent reliability. This lesson helps you implement strategies that catch issues early and maintain code quality effectively.
Overview
As we develop machine learning projects, ensuring that our code is reliable and free of errors is important. One way to do this is through testing automation, which involves using tools and techniques to automate the testing process.
This lesson will discuss code coverage and continuous integration, two important concepts in testing automation. Code coverage helps us understand how much of our code is being tested, while continuous integration allows us to automatically build, test, and deploy our code with each change. Using these techniques, we can ensure that our code works as intended and catch any issues early on in the development process.
Test coverage
Test coverage refers to the amount of code tested during the testing process. It helps us understand how much of our code is being tested and identify any areas that may not be thoroughly tested. By having a high test coverage, we can have confidence in the reliability of our code and ensure that it performs as expected.
Note: Test coverage is an important indicator regarding simplicity, quality, and effectiveness in software testing.
Test coverage and code coverage are often used interchangeably, but some people distinguish between the two terms. Test coverage is a metric reflecting the ...