Organizations continuously strive to provide high-quality solutions quickly in today's fast-paced software development environment by using automation testing over manual testing.
The shift from manual testing to test automation is clear, but it's crucial to defend the benefits of test automation. It is common knowledge that not all test scenarios can be automated, which is why businesses employ a combination of manual testing and automation testing strategies. Because (wherever applicable) automation testing takes precedence over manual testing, it's crucial to keep an eye on the short- and long-term cost reductions of test automation.
Enterprises should regularly assess the Return on Investment (ROI) of test automation efforts, regardless of the web automation framework used (e.g., Selenium, Cypress, Playwright, etc.). This idea holds true for automation testing across various platforms, including desktops and mobile devices.
What are Test Automation Metrics?
Test Automation Metrics are software testers that utilize a set of measurements to evaluate the success of their automation efforts. With the help of these metrics, you can assess everything from the effectiveness of your testing efforts and the quality of new product releases to the overall success of your team's software development lifecycle (SDLC) as well as the overall health of your application.
These metrics serve as measurements that QA engineers, developers, and other stakeholders can use to monitor testing progress, improve their quality assurance procedures, and ultimately help inform several important decisions regarding test strategy, resource allocation, and release readiness.
Test Automation Metrics and Key Performance Indicator (KPI) Types
-
Metrics for Test Coverage
Test coverage refers to the extent to which your test cases or suites validate your application’s functionality and ensure that you have developed tests for every module of your application. The objective is to maintain a 100% mapping of requirements to test cases.
Below given are some metrics defined as part of the Test Coverage
-
Test execution progress
It gives a general notion of how many test cases have been executed overall versus how many are still pending.
Formula: Test Execution Coverage = (Total number of executed test cases or scripts / total number of test cases or scripts planned for execution) * 100.
-
Functional Coverage
Specifies how much of the business and functional criteria are covered by the test plan. This measure is calculated by dividing the number of functions run by a test suite by the overall number of functions in the software being tested.
-
Risk Coverage
You may determine how much of your company's risk is covered by test cases with the help of risk coverage. Measuring risk coverage provides you with information about:
-
How extensively have you tested your biggest business risks?
-
Whether your biggest risks are as per expectation?
-
The percentage of your business's risk that is never tested
2. Test effort
Test effort metrics help you estimate how many tests a product requires, how long it will take to finish all of your tests, and how long it will take to fix bugs. Many measures are used to evaluate test efforts:
-
Number of tests in a given time frame
This measure seeks to inform you of the number of tests finished in a specific amount of time. This aids developers in creating future testing schedules.
Formula: Number of tests in a specific time period = Number of tests executed/Total time
-
Measure the effectiveness of the design
This metric seeks to evaluate the design efficiency of the executed test. The amount of time it takes for a team to create a test for a given requirement fully is known as test design efficiency.
Formula: Test Design Efficiency = Number of tests designed / Total time
-
Average time to test a bug fix
This statistic aids in determining the amount of time spent testing a bug fix.
Formula: Average Time to Test a Bug Fix = Total time between bug fix & retest for all bugs / Total number of bugs
-
Number of bugs discovered per test
The number of bugs per test, also known as the bug discovery rate, quantifies how many bugs (or defects) are discovered at each testing stage.
Formula: Number of bugs per test = Total number of defects / Total number of tests
-
Defect Distribution
The defect distribution metric aids in your understanding of the various product flaws, how testing relates to them, which areas of your software (or process) are prone to defects, and finally, where to concentrate your testing efforts.
Metrics for defect distribution include the quantity, proportion, or severity of defects broken down into other groups, such as severity, priority, test type, and so forth.
3. Defect Detection and Recovery
The defect detection and recovery measure keeps track of how long it takes to locate and address problems in your product. Mean time to detect defects (MTTD) and Mean time to resolution (MTTR) are the two most important metrics in this category:
-
Mean time to detect defects
The "mean time to identify defects" or "MTTD" refers to the typical amount of time your company needs to find faults in a product.
Formula: MTTD = Amount of time spent rectifying defects / Total number of defects located
-
Mean time to resolution
The mean time to resolution, or MTTR, is the typical time it takes a business to fix a flaw that affects end consumers.
Formula: MTTR = Total time spent fixing a defect / Total number of defects found
-
Defects per requirement
This metric records the number of flaws or defects found during testing for each unit. This could help developers identify which requirements would need additional work to operate correctly and efficiently.
-
Test team metrics
Test team metrics track how much time is spent testing by teams and individual team members. These indicators aid in your comprehension of team dynamics, such as whether the test team members' workloads are distributed equally and whether any team members require further explanation. Also, it promotes knowledge sharing among team members. Metrics for the test team include:
-
The number of defects reported and rejected
This measure helps QA leads assess team members and distribute workloads by revealing how many faults each team member is identifying.
-
Testing Return on Investment (ROI) Metrics
Calculating the entire cost of testing, the total cost of each testing component, and the ROI of your organization's testing processes is made easier with testing ROI metrics. Developers and QA leads can use these KPIs to budget for future testing costs and evaluate the effectiveness of their testing procedures. The amount of effort and time consumed can also be tracked with their assistance.
-
Cost per bug fix
The amount spent on a defect by a developer determines the cost per bug fix.
Formula: Cost per bug fix = Hours spent on a bug fix * developer’s hourly rate
-
Cost of not testing
This metric enables you to calculate the costs of not testing your product. The "cost of not testing" is the expense of rewriting newly released features that must be changed. It also refers to more irrational things like falling consumer loyalty, lost profits, an increase in customer support requests, and product failures.
-
Schedule variance
The time discrepancy between the intended and actual testing times is known as the schedule variance.
Formula: Schedule variance (if testing finishes earlier than planned) = Planned schedule – Actual schedule
Schedule variance (if testing finishes later than planned) = Actual schedule – Planned schedule
Best Practices for Using Test Automation Metrics
It's critical to adhere to recommended practices to maximize the benefits of testing automation metrics. They consist of:
-
Start Small
Start small and concentrate on a few essential metrics when integrating testing automation metrics.
-
Set Goals
Set specific objectives for your testing automation metrics and monitor your development over time.
-
Use Automation to its Full Potential
Using automation to its fullest extent is crucial for maximizing testing automation metrics. You can leverage the true potential of automation on the cloud.
Digital experience testing cloud like LambdaTest lets you perform cloud-based automation testing of websites and mobile apps on an online device farm of 3000+ real browsers, devices, and OS combinations. You can run test scripts on its real device cloud to validate scenarios in real user conditions.
Common Mistakes to Avoid with Test Automation Metrics
-
Not Overcomplicate Metrics: Keep your metrics simple by not measuring too many things at once.
-
Not Relying Too Much on Metrics: Metrics for testing automation are valuable, but they shouldn't be relied upon too much. To ensure that your application satisfies the highest standard of quality, metrics should be used in conjunction with other testing techniques.
Conclusion and Next Steps
Measuring the results of your automation efforts through testing provides a way to pinpoint areas that need improvement. You can streamline the testing process and ensure that your application is of the utmost quality by tracking your metrics over time and using them to define targets. Remember to adhere to best practices, avert typical errors, and make the most of testing automation metrics.
Consider checking out tools like Selenium, TestRail, and JMeter to assist you in measuring your testing automation metrics. You can harness the power of testing automation metrics and advance your testing procedure with the correct tools and a dedication to continuous development.