Top 10 Metrics to Measure for Test Automation

As the percentage of companies using test automation for maintaining the software quality in a fast-paced Agile and DevOps environment is increasing, the need to measure its effectiveness and ROI is also increasing. Test automation metrics paves the way for determining the return on investment along with an understanding of the areas of test automation which are working and which are not.

Early and frequent execution of automation leads to a higher return on investment, as most of the issues are fixed before being found by the end consumer.

Here are the top 10 metrics you should watch out for to measure the efficacy of test automation:

  • Total Test Duration: Total test duration helps in determining the total time taken to run the automated tests. It is a significant metric which gives you an indication about the efforts and resources the testing team needs to put in to avoid delays


  • Automation Code Coverage: This metric puts light on the amount of code which is covered by the automation. It is crucial to validate the relevance of automation by determining the worth and efficiency of the test automation efforts.


  • Test Effectiveness Based on Defects: This metric counts the number of defects which were found during the test execution phase which helps the team compare the tested product with the previous releases and make necessary changes in the quality.


  • Test Execution: It gives the total number of tests executed during a build. This metric shows whether the executed tests were performed as per the expectations or not. To easily understand the data, graphs and charts can be used for depicting the total number of executions in the form of categories such as passed, failed, incomplete, or blocked.


  • Percentage of Passed Tests and Failed Tests: This metric gives the count of the number of tests that have been passed or failed from the total tests planned to run. The metric gives a better chance to overview the testing progress. The number of passed failed, and incomplete tests could be shown in the form of a bar graph. The data can then be compared with the different releases on different days.


  • Percentage of Broken Builds: As automation tests can break the build in an agile development process, this metric will help in determining the broken builds that occurred due to failure in tests and code quality. The percentage of broken builds indicates the level of code quality and engineering practices. Higher the percentage of broken builds, lower the code quality and it shows that engineers are not much efficient in maintaining the accuracy and stability of the code.


  • Useful Results vs Irrelevant Results: It compares the useful and irrelevant results in automation testing. Useful results indicate that the test failed due to a defect whereas irrelevant results indicate that the reason behind the test failure could be changes made to the product or test environment-related issues. The metric can help the teams spend their time and testing efforts on useful results. It also gives an idea regarding the source of the irrelevant results to help reduce them as compared to useful results in order to improve automated testing.


  • In-sprint Test Automation: This metric helps in measuring the percentage of automation achieved during the sprint and the percentage that was picked up later by the automation team. It can help improve the quality of various root causes such as quality of backlog grooming and stability of use cases in the sprint, quality of automation framework design, and amount of API testing vs UI testing.


  • Equivalent Manual Test Effort: It gives you the amount of effort required to run the case manually. This metric is very useful especially in those organizations which rely on both manual and automated tests and in those who have recently switched from manual to automation. It helps in determining the benefits of running automated tests in relation to manual tests.


  • Requirements Coverage: Requirements coverage metric shows the features that have been tested and the number of tests that are dependent on the user requirements. This metric helps in tracking the number of features that have met the user’s requirements and how many of them are covered by automation tests.


These were the top 10 metrics from the many possible automated test metrics that are crucial to measuring the efficacy of test automation. Though metrics are crucial for tracking and understanding the test automation picture, sometimes it may give misleading information.

Hence along with the top test automation metrics, it is crucial to discuss the key challenges which you may face while using them:

  • Automation Testing metrics can give false or misleading information about its efficacy
  • Sometimes metrics include results that are irrelevant such as test failures due to changes made in the application
  • Unit tests are easy to track but measuring integration and acceptance tests is a bit complex
  • Metrics require careful and deep analysis in order to get benefitted from them

A good metric should be simple, objective, and easy to measure. It’s important to not only keep these challenges in mind but also to overcome them by carefully examining these metrics and churning out useful information regarding your test automation.

Test automation metrics not only keep the process efficient but also help achieve DevOps goals now being referred to as DevTestOps by identifying new areas that can be automated and reducing the time consumed in QA test automation services. The metrics discussed in this article can help you eliminate the bottlenecks in the process to help you achieve your end goal.

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