Code Coverage vs Test Coverage: Differences you need to know

test coverage vs code coverage
By , Head of Test Automation

Code coverage and test coverage are two of the most important metrics in software testing, but they often get confused with each other. So, what’s the difference?

In this article, we will take a closer look at the distinctions between code coverage vs test coverage. We will also discuss why these metrics are important, and how to use them to improve your software’s quality.

What is Code Coverage?

Code coverage is a fundamental metric in software testing that measures the proportion of your codebase that has been executed by your test suite. It helps you evaluate the effectiveness of your testing efforts by revealing which parts of your code have been tested and which parts remain untested.

For instance, if you have tested 80% of your code, your code coverage will be 80%. Increasing your code’s coverage is generally a good practice, because it helps uncover potential bugs and it ensures that your code behaves as expected in a wide range of scenarios.

What is Test Coverage?

Test coverage is a measure of how much of a software application's code and functionality has been tested. It helps assess the thoroughness of testing by quantifying the percentage of code statements, branches, and requirements that have been exercised by tests. In essence, it tells you which parts of your software have been checked for correctness and which areas might still remain untested.

Test coverage metrics help identify gaps in your testing efforts, ensuring that critical parts of the software are not overlooked. Its goal is to achieve a high coverage of your tests to minimize the risk of undetected bugs in the software, although 100% coverage is often difficult to achieve and it’s not always necessary.

Code Coverage vs Test Coverage

Take a look at the comparison table below that demonstrates the distinctions and similarities between Code Coverage vs Test Coverage in the context of software development.

AspectCode CoverageTest Coverage
DefinitionIt helps to measure how much of the source code is executed by tests. It focuses on lines, branches, and statements covered by tests.It helps to evaluate the effectiveness of test cases in assessing the software’s functionality and quality. It measures the proportion of requirements or features tested.
PurposeIt helps assess the thoroughness of your testing efforts by identifying untested code segments and ensuring they are executed during testing.It ensures that test cases are designed to verify the desired functionality, including edge cases, boundary conditions, and potential failure scenarios.
ScaleIt is typically measured at the code level, such as lines, branches, and statements, making it a lower-level metric.It is focused on higher-level functionality, covering use cases, requirements, and user scenarios, making it a higher-level metric.
MetricsCommon metrics include: Line Coverage, Branch Coverage, Statement Coverage, and Path Coverage.Common metrics include: Requirement Coverage, Use Case Coverage, Functional Coverage, and Scenario Coverage.
Use in DebuggingIt is useful for identifying untested code, which can help pinpoint areas that require additional testing and uncover potential bugs.It is helpful in determining whether the test suite is designed to catch specific defects and validate the software’s functionality according to requirements.
Focus on Test CasesIt does not focus on the quality, intent, or relevance of individual test cases. It primarily checks if they execute code.It emphasizes the quality of test cases, ensuring that they are designed to cover various scenarios and verify functionality accurately.
Integration with CI/CDThis metric can be integrated into Continuous Integration/Continuous Delivery (CI/CD) pipelines to ensure that new code doesn’t reduce overall coverage.This metric can be integrated to assess the effectiveness of test suites in CI/CD pipelines, identifying areas that require more attention.
Development ImpactPrimarily impacts developers by encouraging them to write testable code and identify untested sections for better testing strategies.Influences both developers, who write meaningful test cases, and testers, who ensure comprehensive coverage of functionality.
Importance in QAIt is vital in QA, but it may not directly address whether the software meets the user requirements, as it doesn't focus on user scenarios or use cases.It is fundamental in Quality Assurance (QA) since it ensures that the software functions correctly and meets user expectations based on requirements.
Common ToolsPopular tools include JaCoCo, Cobertura, and Istanbul for various programming languages.Tools like TestRail, QTest, and Zephyr are used for managing test cases and requirements in the context of test management.

Which metric is more important: Code Coverage vs Test Coverage

The answer to this question depends on your perspective.

Code coverage is easy to measure and it can be a good way to identify areas of code that are not being tested. However, a high coverage level of code does not guarantee that your software is well-tested. For example, you could have a high coverage level of your code, but your tests could all be testing the same happy-path scenarios. This would mean that your code is well-exercised, but your software is still vulnerable to bugs in edge cases.

Test coverage is a more difficult metric to measure, but it is a better indicator of the quality of your test suite. To achieve a high coverage of your tests, you need to think about the requirements of your software and write tests that cover all of those requirements. This can be a time-consuming process, but it is essential for building high-quality software.

Let’s take a look at the real-life analogy of Code Coverage vs Test Coverage. Code coverage is like measuring the number of times you go to the gym. Test coverage is like measuring the quality of your workouts.

It’s easy to go to the gym every day, but if you are not doing effective workouts, then you are not going to achieve the results you desired. The same is true for code coverage. It is easy to achieve a high coverage level of your code, but if your tests are not well-designed, then they are not going to find bugs in your software.

Focus on writing tests that cover your requirements, and you will be well on your way to building high-quality software.


In the ever-evolving landscape of software development, the Code Coverage vs Test Coverage distinction is not only about technical nuances but it’s also about the critical differences in delivering high-quality applications.

Code Coverage illuminates the dark corners of your codebase, exposing areas that require testing and enabling developers to ensure that every line of code has its moment in the spotlight. It’s not a panacea, however. Achieving a high coverage of code doesn’t guarantee that your software is going to become bug-free or that all user scenarios have been explored.

Test Coverage, on the other hand, places the user experience at the forefront, validating that your software meets its requirements and user expectations. But achieving a high сoverage of your tests doesn’t necessarily mean that every line of code will get examined.

Afterall, it’s not just about Code Coverage vs Test Coverage, it’s rather the synergy between these terms, since together they help build resilient, user-centric software. It’s not a matter of choosing one over the other but understanding how they both complement each other.

With this knowledge in mind, you are now better equipped to make informed decisions, bolster your development strategies, and deliver software that not only functions flawlessly but also aligns with user needs. Coverage of your code and coverage of your tests, in harmony, pave the way for software that not only works but truly shines in the eyes of its users.

Sergey Zagriychuk
Head of Test Automation
Sergei Zagriychuk has over 10 years of experience in software development and automated testing. At Solvd, he sets up test automation from the ground up on various projects. Sergei contributed to developing end-to-end test automation solutions by Solvd: Carina, a test automation framework, and Zebrunner, a reporting and analysis tool.

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