Automated testing is an essential part of the software development process, as it helps ensure that a software application is working as intended and is free of defects. However, it’s important to track and measure the performance of your automated testing efforts to ensure that they are effective and efficient. In this blog post, we’ll take a look at some key metrics that you should consider when evaluating your automated testing efforts.
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Test coverage
Test coverage is a measure of how much of the codebase is being tested by your automated tests. It’s important to have high test coverage to ensure that as much of the codebase as possible is being thoroughly tested.
There are several ways to measure test coverage, including:
- Line coverage: This measures the percentage of lines of code that are being executed by your tests.
- Branch coverage: This measures the percentage of conditional statements (e.g.,
ifstatements) that are being executed by your tests. - Function coverage: This measures the percentage of functions or methods that are being called by your tests.
To increase test coverage, you can write more tests or refactor your existing tests to cover more code.
Test execution time
Test execution time is a measure of how long it takes for your automated tests to run. It’s important to keep test execution time as low as possible to ensure that your tests are efficient and can be run frequently.
To reduce test execution time, you can:
- Optimize your tests to run more efficiently.
- Run tests in parallel to take advantage of multiple CPU cores.
- Use faster hardware or a faster test execution environment.
Automated test vs manual tests:
This metric is useful in order to understand the percentage of test cases that we have automated and the percentage of tests cases that need to be automated, in other words, how far we are from reaching 100% automation coverage on a certain project.
Test automation velocity vs test creation velocity
The automation velocity is how many test cases are we able to automate in a certain amount of time, and the test creation velocity is how many new test cases are created in the same timeframe. If we are using sprints, we can see if we need more automation resources in order to catch up with the manual test case backlog, or if we are in good shape with that.
% Green, % Yellow and % of red
When we talk about green, yellow and red test cases, we are talking about those test cases that are passing (green), failing (red) and those that require some kind of maintenance (yellow). If we trust our test suite, we can say that those tests in green and red are working fine, at least those that fail in red are failing because we have an issue in the application, so the test case is doing what it´s expected to do. On the other hand, yellow tests cases are those that need to be fixed because the application changed and we have to update our functional tests definitions.
Fixing our yellow tests should be more important than creating new ones, nobody will trust a test suite with many yellow tests at the end of the day, so creating new ones is senseless.
Test execution time
This metric allows us to understand how much time our test execution takes, for example, for a specific environment, like mobile devices. This is important if we want to understand if we need more computing power to parallelize more test suites to reduce execution time.
Test pass/fail rate
The pass/fail rate is a measure of how often your automated tests are passing or failing. A high pass rate is generally desirable, as it indicates that your tests are effective at catching defects. A low pass rate may indicate that your tests are not thorough enough or that there are issues with the code being tested.
To improve the pass rate of your tests, you can:
- Write more thorough tests that cover more scenarios.
- Refactor your tests to be more reliable and less prone to false positives or negatives.
- Fix defects in the code being tested to prevent them from causing test failures.
Test maintainability
Test maintainability is a measure of how easy it is to maintain and update your automated tests over time. It’s important to have maintainable tests to ensure that they can be updated and modified as the codebase changes.
To improve the maintainability of your tests, you can:
- Use clear and concise test names and descriptions.
- Use a consistent coding style and structure in your tests.
- Use helper methods and utility functions to reduce duplication and complexity in your tests.
Top test automation metrics to track in 2023
There are several key metrics that can be used to measure the effectiveness of test automation. These include:
- Test coverage: This metric measures how much of the codebase is covered by automated tests. A high test coverage indicates that a large percentage of the code has been tested, which can help to identify potential issues and reduce the risk of bugs.
- Test execution time: Automated tests should be run regularly, so it’s important to ensure that they run quickly. Long test execution times can slow down the development process and make it harder to get feedback on changes.
- Test pass rate: The percentage of automated tests that pass is a key metric to track. A low pass rate could indicate that there are issues with the tests or with the code being tested.
- Test reusability: Automated tests should be designed to be reusable, so that they can be run in different environments or on different versions of the code. Tracking the reusability of tests can help to identify areas where tests may need to be updated or refactored.
- Test maintainability: As codebase changes, automated tests may need to be updated to ensure that they continue to be effective. The maintainability of the tests is an important metric to track, as it can help to identify areas where tests may be breaking or becoming outdated.
By tracking these and other test automation metrics, it’s possible to get a clear picture of the effectiveness of the tests and make any necessary improvements. This can help to ensure that the automated tests are providing value and are helping to improve the quality of the product.
Conclusion: Automated testing metrics
Tracking and measuring the performance of your automated testing efforts is essential for ensuring that they are effective and efficient. By keeping an eye on key metrics like test coverage, execution time, pass/fail rate, and maintainability, you can continually improve your automated testing efforts and ensure that they are providing value to your software development process.