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What advanced QA automation metrics can you use for evaluating testing tools and frameworks?

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QA automation is not only about writing and executing test scripts, but also about measuring and reporting the results. To evaluate the effectiveness and efficiency of your testing tools and frameworks, you need to use advanced QA automation metrics that go beyond the basic ones like test coverage, pass/fail rate, and defect density. In this article, you will learn about four advanced QA automation metrics that can help you assess the quality, performance, reliability, and maintainability of your testing process and products.

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Viktor Nedelko

QA Engineer proficient in Technical Leadership

1 Quality Metrics

Quality metrics measure how well your test scripts detect and prevent defects in your software products. Fault Detection Percentage (FDP) calculates the ratio of defects found by automation to the total number of defects found by any method, indicating the effectiveness of your test scripts. Defect Removal Efficiency (DRE) determines the ratio of defects removed by automation to the total number of defects injected during development, showing how well your test scripts are at preventing bugs from reaching the end users. Mean Time to Failure (MTTF) calculates the average time between failures of your software products, indicating their reliability and stability.

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Viktor Nedelko

QA Engineer proficient in Technical Leadership
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Depending on certain factors (Automations place in SDLC, release schedule, iterating practises, test documentation practises), you might find yourself in a situation where automation development becomes the testing process, which detects faults before the script actually becomes a part of an automated test suite. That might be caused by deeper involvement of automation engineer, lack of test documentation on feature testing stage or even simply due to automated scripts being faster than manual testing. Also you can perform synthetic monitoring (periodic smoke test runs on prod) which can cause issues caused by 3rd party integrations, internal administrative errors, load spikes, etc. All these catches should be included when calculating FDP.

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2 Performance Metrics

Performance metrics measure how fast and scalable your test scripts are. Test Execution Time calculates the total time taken by your test scripts to run on a given environment, and a low result indicates that they are fast and optimized. Test Throughput calculates the number of test cases executed per unit of time, with a high result signifying scalability. Resource Utilization measures the amount of resources consumed by your test scripts during execution, and a low utilization suggests that your test scripts are efficient and do not cause performance bottlenecks.

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Viktor Nedelko

QA Engineer proficient in Technical Leadership
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Talking about performance, especially when it concerns CI/CD, resource utilisation should not exclusively be regarded as preferably low. Utilising parallelisation might require a bit more juice, but will prove to be faster in most cases, especially at scale. So also keep in mind that while you should aim for maximum performance, you should employ available resources to the fullest. Another aspect to consider, is test runner environment resources. Again, talking about CI/CD, sometimes different CI runners prove to perform differently on the matter of how fast they delegate necessary resources, so making configs flexible depending on projects / runners / CI vs local / employed frameworks - might prove to be a fruitful experiment.

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3 Reliability Metrics

Reliability metrics measure the consistency and dependability of your test scripts. Test Failure Rate calculates the percentage of test cases that fail during execution, with a low rate indicating that your test scripts are reliable. Test Flakiness calculates the percentage of test cases that produce different results when executed multiple times, with a low rate indicating that your test scripts are dependable. Lastly, Test Reusability calculates the percentage of test cases that can be reused across different modules, features, or scenarios. A high rate indicates that your test scripts are modular and flexible and do not require frequent changes or updates.

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4 Maintainability Metrics

Maintainability metrics measure how easy and cost-effective it is to maintain and update your test scripts. For instance, the Test Complexity metric calculates the degree of difficulty or intricacy of your test scripts, while the Test Code Quality metric assesses the quality of your test code. Additionally, the Test Automation ROI metric evaluates the return on investment of your test automation. Low Test Complexity and high Test Code Quality and Test Automation ROI indicate that your test scripts are simple to understand and modify, clean, organized, well-commented, and worth the effort and resources.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What advanced QA automation metrics can you use for evaluating testing tools and frameworks?

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2

3

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5

1 Quality Metrics

2 Performance Metrics

3 Reliability Metrics

4 Maintainability Metrics

5 Here’s what else to consider

Process Automation

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What advanced QA automation metrics can you use for evaluating testing tools and frameworks?

1

2

3

4

5

1 Quality Metrics

2 Performance Metrics

3 Reliability Metrics

4 Maintainability Metrics

5 Here’s what else to consider

Process Automation

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