📌 Test Coverage Metrics Summary

Test coverage metrics are measurements that show how much of your software’s code is tested by automated tests. They help teams understand if important parts of the code are being checked for errors. By looking at these metrics, teams can find parts of the code that might need more tests to reduce the risk of bugs.

🙋🏻‍♂️ Explain Test Coverage Metrics Simply

Imagine checking your homework to make sure you did every question. Test coverage metrics are like a checklist showing which questions you checked and which you missed. The more you check, the less likely you are to miss mistakes.

📅 How Can it be used?

A team uses test coverage metrics to ensure all critical features of an app are properly tested before release.

🗺️ Real World Examples

A banking software team tracks test coverage metrics to ensure their transaction processing code is covered by tests. If the metrics show a low percentage, they add more tests to reduce the risk of missed bugs that could affect customer accounts.

A team building an online shopping website reviews coverage reports and notices their payment gateway code is not well tested. They create additional tests for payment scenarios, increasing confidence that transactions will work correctly.

✅ FAQ

Test coverage metrics show how much of your software has been checked by automated tests. They help you see if important parts of your code are being tested, which can highlight spots that are more likely to have hidden bugs. By keeping an eye on these numbers, teams can make sure their tests are doing a good job of protecting the software from errors.

A high test coverage means that a larger portion of your code is being tested, but it does not guarantee that your code is completely bug-free. Some problems might still slip through if the tests are not checking for the right things. Test coverage is a helpful guide, but combining it with smart test design and regular code reviews gives you the best chance of catching issues.

By reviewing your test coverage metrics, you can spot areas of your code that are not being tested enough. This helps you decide where to add more tests, making your software more reliable. It is a practical way to keep track of your testing efforts and make sure you are not leaving any important parts unchecked.

📚 Categories

• Data Science
• MLOps & Deployment
• Model Optimisation Techniques

🔗 External Reference Links

Test Coverage Metrics link

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