What are code coverage metrics?

Code Coverage

Sep 27, 2023

Code coverage metrics are essential tools in the world of software testing. They help developers and QA teams assess the effectiveness of their test suites by measuring the extent to which the application's code is exercised during testing. In this article, we'll explore three fundamental types of code coverage metrics and provide examples to illustrate each one.

Understanding Code Coverage Metrics

Code Coverage is a measure of how much of your source code is exercised or executed by your test suite. It quantifies the extent to which your codebase has been tested and can serve as an indicator of code quality. How you quantify, however, makes all the difference - let's explore the different types of coverage metrics.

Types of Code Coverage Metrics

1. Statement Coverage (Line Coverage):

  • Definition: Statement coverage measures the percentage of executable statements in the code that have been executed during testing. Each line of code is evaluated for coverage.

  • Example: In a piece of source code, the percentage of lines executed during testing would determine the statement coverage. Achieving 100% statement coverage means every line of code was executed by your tests.


def add(a, b):
    result = a + b

If you have a test that calls add(2, 3), it will execute both the assignment statement (`result = a + b`) and the print statement. As a result, statement coverage would be 100% in this case since both statements were executed.

2. Branch Coverage (Decision Coverage):

  • Definition: Branch coverage assesses the percentage of decision points in the code (e.g., if statements, switch statements) where both true and false branches have been tested.

  • Example: When you test a conditional statement like an if-else construct, achieving 100% branch coverage means that every possible outcome of the decision has been tested.


def is_even(num):
    if num % 2 == 0:
        return True
        return False

To achieve 100% branch coverage here, you need tests that cover both the True and False branches of the if statement. So, if you have tests for is_even(4) (True) and is_even(5) (False), you would achieve full branch coverage.

3. Function Coverage:

  • Definition: Function coverage measures whether each function or method has been called or invoked at least once during testing.

  • Example: For a codebase with multiple functions or methods, achieving 100% function coverage implies that every function has been exercised by your tests.


def calculate_sum(a, b):
    return a + b

def calculate_product(a, b):
    return a * b

To achieve 100% function coverage, you need to ensure that each function is called in your tests. For instance, you might have tests like calculate_sum(2, 3) and calculate_product(2, 3) to cover both functions.

Significance of Code Coverage Metrics

Code coverage metrics play a pivotal role in software testing, especially during unit testing. Unit tests are small, targeted tests that validate specific parts of the code, such as functions or methods. Test cases are designed to ensure that different parts of the code are executed, and coverage metrics help measure the effectiveness of these tests.

A comprehensive test suite, coupled with code coverage analysis, provides testers and developers with actionable insights into the codebase. It identifies untested or partially tested areas, commonly referred to as dead code, which may contain bugs or vulnerabilities.

Code Coverage Tools and Automation

To streamline the process of code coverage analysis, various code coverage tools are available, both open source and commercial. These tools provide detailed coverage reports that display the coverage percentage for different parts of the code. Popular code coverage tools include JaCoCo (for Java), coverage.py (for Python), and tools integrated with IDEs like Intellij and Visual Studio.

Automation is a key aspect of code coverage analysis. Automated tests can quickly and consistently execute a set of tests, ensuring that code coverage metrics are regularly updated and actionable.

Types of Testing and Coverage

Different types of testing, such as integration testing and white box testing, utilize coverage metrics to assess the quality of software applications. Coverage metrics are particularly useful in identifying gaps in test coverage and ensuring that new features do not introduce regressions.

Other Coverage Metrics

Beyond the types mentioned above, there are more advanced coverage metrics like path coverage and condition decision coverage. These delve deeper into control structures, conditionals, and complex code paths, providing a more thorough understanding of test coverage.


In the world of software development and testing, code coverage metrics are essential tools for maintaining and improving code quality. By measuring the extent to which different parts of the codebase are exercised, testers and developers can identify areas that need attention, improve the reliability of their software, and ensure that new features are built on a solid foundation. Understanding and implementing code coverage metrics is a fundamental step toward achieving high-quality software applications.

BuildPulse’s Code Coverage allows you to monitor the coverage of your testing suite, and make adjustments to ensure you are releasing robust products. In addition to this, our reports don’t simply outline what is covered, but also what isn’t covered. Using BuildPulse Code Coverage can help you identify the weak links in your codebase and make the appropriate adjustments. Incorporate BuildPulse Code Coverage into your software development process, monitor appropriate coverage metrics for your project, and leverage our automation and code coverage tools to ensure your codebase is well-tested and of the highest quality.

Happy coding and testing!


What is the difference between a flaky test and a false positive?

A false positive is a test failure in your test suite due to an actual error in the code being executed, or a mismatch in what the test expects from the code.

A flaky test is when you have conflicting test results for the same code. For example, while running tests if you see that a test fails and passes, but the code hasn’t changed, then it’s a flaky test. There’s many causes of flakiness.

What is an example of a flaky test?

An example can be seen in growing test suites - when pull request builds fail for changes you haven’t made. Put differently, when you see a test pass and fail without any code change. These failed tests are flaky tests.

What are common causes of flakiness?

Broken assumptions in test automation and development process can introduce flaky tests - for example, if test data is shared between different tests whether asynchronous, high concurrency, or sequential, the results of one test can affect another. 

Poorly written test code can also be a factor. Improper polling, race conditions, improper event dependency handling, shared test data, or timeout handling for network requests or page loads. Any of these can lead to flaky test failures and test flakiness.

End-to-end tests that rely on internal API uptime can cause test flakiness and test failures.

What's the impact of flaky tests?

Flaky tests can wreck havoc on the development process - from wasted developer time from test retries, to creating bugs and product instability and missed releases, time-consuming flaky tests can grind your development process to a halt.

What is the best way to resolve or fix flaky tests?

Devops, software engineering, and software development teams will often need to compare code changes, logs, and other context across test environments from before the test instability started, and after - adding retries or reruns can also help with debugging. Test detection and test execution tooling can help automate this process as well. 

BuildPulse enables you to find, assess impact metrics, quarantine, and fix flaky tests.

What are some strategies for preventing flaky tests?

Paying attention and prioritizing flaky tests as they come up can be a good way to prevent them from becoming an issue. This is where a testing culture is important - if a flaky test case is spotted by an engineer, it should be logged right away. This, however, takes a certain level of hygiene - BuildPulse can provide monitoring so flaky tests are caught right away.

What type of tests have flaky tests?

Flaky tests can be seen across the testing process - unit tests, integration tests, end-to-end tests, UI tests, acceptance tests.

What if I don't have that many flaky tests?

Flaky tests can be stealthy - often ignored by engineers and test runs are retried, they build up until they can’t be ignored anymore. These automated tests slow down developer productivity, impact functionality, and reduce confidence in test results and test suites. Better to get ahead while it’s easy and invest in test management.

It’s also important to prevent regressions to catch flakiness early while it’s manageable.

What languages and continuous integration providers does BuildPulse work with?

BuildPulse integrates with all continuous integration providers (including GitHub Actions, BitBucket Pipelines, and more), test frameworks, and workflows.

Combat non-determinism, drive test confidence, and provide the best experience you can to your developers!

How long does implementation/integration with BuildPulse take?

Implementation/integration takes 5 minutes!

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