Automation testing plays a crucial role in ensuring the delivery of a high-quality product. While the significance of automation tests is widely acknowledged, it is important to determine how to quantify their impact, assess their value in terms of effort and resources, and measure the success of test automation. Outlined below are metrics that can be employed to evaluate the effect of automation on the overall application quality, along with some best practices to generate data for these metrics.

1. Requirements Coverage

The quality of software heavily relies on the accuracy of its defined requirements by the business and project teams. Therefore, requirement coverage becomes a vital measure to determine the extent of application testing. By mapping test cases to requirements, teams can verify that all desired functionalities are adequately tested. This approach mitigates risks, facilitates traceability, and helps achieve the desired quality standards for the software. There are several approaches to calculate these figures, all aiming to ascertain whether all product features have been automated.

  • It is recommended to categorize automation tests based on the criticality of business flows, such as critical/high/medium/low.
  • Mapping automation tests to documented test cases enhances visibility and traceability of test execution with respect to product features.

2. Test Execution Duration

One key metric for measuring the success of automation is the runtime of tests. Total test duration quantifies the time required to execute automated tests. Although this metric does not directly measure quality, extensive and time-consuming automation suites can deter developers due to long waiting periods or time-consuming reporting. By understanding the factors that influence execution duration and implementing optimization strategies, teams can significantly improve the efficiency and speed of their automated testing processes. Prioritizing test cases, leveraging parallel execution, optimizing individual test cases, managing the test environment and data, and adopting continuous integration and continuous testing practices are all key steps towards achieving optimized automation test execution duration.

  • Consider breaking down larger test suites to achieve faster reporting while ensuring critical tests are not missed.
  • Parallel test execution should be contemplated during the framework design phase, taking into account appropriate design patterns, thread-safe data structures, and test data management.
  • Integrate automation tests into a continuous integration and continuous testing pipeline. By automating the execution of tests triggered by code changes, teams can identify defects earlier and reduce the overall execution duration.

3. Test Pass or Fail Percentage

This metric indicates the percentage of tests that have recently passed or failed in relation to the total planned tests. Analyzing the results of test suites over time provides an overview of testing progress and instills confidence in the automated tests. This metrics provides insights into the success rate of executed test cases and highlights areas that require attention or further testing. By considering factors such as test case quality, test data, test environment, and test execution strategy, organizations can optimize their testing processes and improve the pass or fail percentage. Regular monitoring and analysis of this metric contributes to overall software quality improvement, defect identification, and efficient resolution.

  • The objective should not be limited to achieving a 100% pass rate but also encompassing adequate coverage and quick turnaround time for analyzing failures.
  • Flaky tests are inevitable, often caused by factors like poor test data, environments, or coding practices. It is advisable to have separate configurations for rerunning or retrying such tests, ensuring test execution duration remains manageable.
  • A stable and consistent test environment is essential for reliable test results. Environmental issues, such as hardware or software inconsistencies, can lead to false failures and impact the overall pass or fail percentage.

4. Equivalent Manual Testing Efforts

EMTE measures the effort required to execute tests manually compared to automating them. This metric enables more accurate planning of release timelines and resource allocation. It serves as a metric and allows QA professionals to focus on other aspects of quality and testing. Understanding the factors influencing equivalent manual testing efforts helps organizations strike a balance between automation and manual testing. By identifying suitable test candidates, assessing risks, adopting an iterative approach, and continuously evaluating the testing strategy, organizations can optimize their testing efforts and ensure comprehensive software quality assurance.

  • It is essential to recognize that automation does not aim to replace manual testing entirely but rather saves time by avoiding repetitive tests for each release.
  • A significant portion of exploratory manual testing should still be conducted to add value to the build’s quality.

5. Script Maintenance Time

Every piece of automation test code requires maintenance, which may involve updating UI locators, adapting to changes in API contracts, addressing intermittent failures, or resolving discrepancies between local and Jenkins environments. This metric quantifies the time invested in the maintenance process, providing insights into the overall value of test automation for the project. Recognizing the factors that impact maintenance efforts and adopting effective strategies can help manage and optimize maintenance time. By emphasizing modular test design, maintaining comprehensive documentation, conducting regular code reviews, leveraging version control, integrating scripts into a CI pipeline, and investing in training, organizations can ensure sustainable and efficient automation script maintenance.

  • Adopt a modular and reusable test design approach. By structuring automation scripts in a modular manner, changes in individual components can be isolated and addressed more efficiently, minimizing the overall maintenance time.
  • If more time is spent on maintenance rather than writing new tests, it may be necessary to assess the framework design, coding practices, and testing environment.
  • It is advisable to allocate time to resolve issues and develop new tests to maintain confidence in the automated tests within the team.


In conclusion, the above-mentioned five points are common and self-explanatory, yet they are often overlooked. Additional metrics can be incorporated based on individual requirements, such as return on investment (ROI), defect distribution, build stability, and execution trends. These metrics should be applied to all layers of automation testing in the test pyramid, whether it involves unit tests, component tests, or end-to-end tests. While these metrics do not serve as quality gates for
the application under test, they reflect the value of automated tests, which ultimately impacts the overall product quality. Poor performance in these metrics may indicate gaps in automation practices, potentially impeding testing progress or creating a false impression that all cases are automated, leading to a decline in product quality.

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