Test automation metrics offer insights into your automation programs' performance, efficiency, effectiveness, and reliability. Here are ten crucial metrics that every test manager should be tracking and monitoring:
Test coverage
Monitoring test coverage in your automation program ensures that your software and applications are adequately tested and reduces the risk of undetected bugs or defects. When measuring test coverage, you can define coverage in three ways:
- Code coverage: Percentage of code lines/blocks/branches executed by automated tests.
- Functional coverage: Percentage of application functions or features tested.
- Requirements coverage: Percentage of requirements or user stories that have corresponding tests.
However you define coverage, remember to use your sprint reviews or retrospective meetings to discuss test coverage reports to identify areas that need more attention. Give priorities to crucial application paths and functionalities or areas with a high number of defect reports. Aside from functional tests, ensure coverage for performance, security, usability, integrations, and other non-functional tests.
Remember that 100% coverage might not always be feasible when considering test coverage goals. Even a high test coverage doesn't guarantee the absence of defects, as you can still have bugs due to missing scenarios or incorrect test logic.
Pass rate
As the name suggests, the pass rate is the percentage of automated tests that pass during a test run. While a high pass rate suggests stability and frequent fluctuations can indicate instability, that is not always the case.
If your automation test program does not have high test coverage, or the automation test cases are not comprehensive, written well or outdated, even a 100% pass rate might not reflect the actual software quality.
Focusing solely on pass rate can divert attention from other essential metrics, such as code coverage, defect density, or response times. The key to using pass rate as a measure of the performance of your automation program is to monitor rates over time to identify trends and to use it in conjunction with other metrics.
Fail rate
Rising fail rates in your test automation can indicate new bugs in the software, especially if no code changes have been included. However, like the pass rate, understanding the context in which the tests were run is important. A higher fail rate might be expected if there were major code changes, new functionality introduced, test issues, or environmental changes and problems.
Use fail rate in conjunction with other metrics, such as test coverage, pass rate, and test execution time, to get a comprehensive view of software and test quality.
Test execution time
Total test execution time is the overall time it takes for the entire test automation suite to run and provides a measure of the test suite's efficiency. In addition to monitoring how long it takes to run the entire suite, monitor how long it takes to run different suites (e.g., smoke tests, regression tests, integration tests). These can help to pinpoint where you may have to focus on software improvements or reviewing your test automation cases.
Also, look at how test execution time changes over time. If you are seeing an increasing trend and are not adding to your test automation suite, you may want to investigate whether there are performance issues or whether the software is getting more complex. Evaluating the execution time of individual tests may help identify cases needing optimization or refactoring.
Common conditions that increase the duration of test runs include complex test scenarios, high test volume, inefficient test code, UI automation, database operations, external dependencies, network latency, flaky tests, heavy setup/teardown requirements, memory leaks, fixed sleeps or waits, test environment instability, heavy logging or reporting, lack of test maintenance, inefficient test tools or frameworks, heavy graphics renderings/animations and resource contention.
Test flakiness
Test flakiness refers to tests that exhibit inconsistent results—passing in one run and failing in another without any changes to the codebase. Flaky tests undermine the primary purpose of automation, which is to offer fast, reliable feedback to developers.
Tracking and addressing test flakiness is necessary to maintain an automation program's credibility and ensures that automated tests provide consistent and actionable insights into the software's quality and behavior.
False positive rate
The false positive rate is the percentage of tests that incorrectly identify a defect or issue when there isn't one. They compromise the testing and automation program's trustworthiness as they doubt the reliability of the entire suite. This skepticism can also lead to genuine defects being overlooked or dismissed.
In addition to deteriorating the trust in test automation programs, false positives also contribute to wasted time and resources to investigate the cause of a failed test. This can be extremely disruptive in agile environments or those with continuous integration/continuous deployment (CI/CD) pipelines.
Regular reviews and refinements of the automation suite are essential to mitigate the impact of false positives. Implementing a feedback loop where developers can quickly report false positives and the QA team can adjust tests will reduce their occurrence. In the long run, managing and aiming to reduce the false positive rate is crucial to ensure a testing and automation program's efficiency, credibility, and overall success.
Also Read: Salesforce Test Automation: Getting Started and Smart Tools
False negative rate
The false negative rate is the number of tests incorrectly passed. This metric is especially concerning because it can lead to genuine defects being released into production and provides a false assurance of software quality. A high false negative rate may arise from poorly designed test cases, inadequate application functionality coverage, or issues with the automation tools themselves.
False negatives can be particularly detrimental in healthcare, finance, aerospace, and other critical applications where undetected defects can cause operational risk, reputational damage, increased maintenance costs, or significant financial penalties.
Addressing a high false negative rate requires regular reviews and refinements of test cases, enhancing test coverage, and possibly re-evaluating the automation tools. Establish feedback loops between developers, testers, and stakeholders to share insights and improve the testing process collaboratively.
Defect Detection Rate (DDR)
Defect detection rate (DDR) measures the efficiency of the testing process in identifying defects within the software and provides valuable insights into the effectiveness of an organization's testing efforts. It is calculated by dividing the number of defects detected by the number of test cases executed over a specific period.
A high DDR might indicate that the testing process is thorough and effective at uncovering defects, while a low DDR might hint at potential gaps in the testing automation process. However, interpreting DDR requires context. A high DDR during the early phases of software development might be expected. DDR should be complemented with metrics like test code coverage, pass rate and false positive rate for a holistic view of software quality.
Cost of test automation
The cost of test automation quantifies the financial resources allocated to implement, maintain, and run automated tests. This metric is crucial for gauging the return on investment (ROI) of automation efforts and determining whether the benefits of automation outweigh its costs. Several factors contribute to the cost of test automation, including:
- Automation tool acquisition costs
- Infrastructure (hardware, virtual machines, cloud services, etc.) costs
- Time for testers and developers to write, review and maintain automated test scripts
- Time and costs to train team members to use new tools or learn automation techniques
- Maintenance costs for software tools.
Investing in test automation can initially seem high, but the long-term benefits often justify the expense. Automated tests can be executed frequently and consistently, leading to early defect detection, and reduced manual testing efforts. To ensure that the investment in automation continuously yields positive returns, organizations must continuously monitor and optimize the cost of test automation.
Return on Investment (ROI)
Tracking the ROI quantifies the value derived from testing efforts relative to the costs incurred. It helps to justify the investments in an automation program, provide guidance on how to scale, optimize, or pivot the testing strategy, and guide when it is time to look at new automation options.
To calculate ROI, start by calculating the total expenditure of the testing program, including licenses, infrastructure costs, personnel salaries, training, and maintenance. Next, assess the tangible benefits from testing, like faster release cycles, reduced defect rates in production, savings from early defect detection, and hours saved from manual testing.
ROI = (Benefits from test automation - Cost of test automation) / Cost of test automation
To ensure that your ROI metric accurately reflects actual value, continuously monitor and recalibrate costs, tools, strategies, project scope and benefits. Like other metrics, ROI should not be viewed in isolation. A low ROI rate might be due to an immature automation program. Consider other qualitative and quantitative metrics with ROI.
Use BotzAutomation as your test automation tool
BotzAutomation is a cost-effective automation tool that allows organizations to use one tool for their end-to-end testing. It includes testing capabilities like API, database, ERP, and desktop and mobile testing. Many of the features allow organizations to keep their automation costs down, including:
- No code automation eliminates the need to learn the latest or legacy scripting languages
- Easy-to-use drag-and-drop canvas allows all employees to add to the test suite
- Support for multiple platforms and business process automation, which allows the organization to increase their test coverage with a single tool
- Ability to update components of target applications, rather than redoing the entire test case or updating individual test cases to reduce automation ongoing maintenance costs.