What are the most important test automation metrics to track and report?

o Pass/Fail Rate: The ratio of passed tests to the total number of tests. o Test Execution Time: The total time taken to execute all tests. o Test Suite Execution Time: Time taken for executing the entire suite of tests. o Test Flakiness: The frequency of tests that produce different results without changes to the codebase o Test Stability: The stability and reliability of test scripts over multiple runs. o False Positives/Negatives: The number of incorrect test results (e.g., tests that pass incorrectly or fail incorrectly).

The most important test automation metrics to track and report include test coverage, which measures the extent of the codebase tested by automation; test execution time, which helps assess efficiency and identify bottlenecks; and test pass/fail rates, indicating the stability and reliability of the application. Additionally, track defect density to understand the number of issues found per test, and the rate of test automation ROI to evaluate the cost-effectiveness of automation efforts. Monitoring test flakiness helps identify unreliable tests, while coverage of critical functionalities ensures that essential features are thoroughly tested.

Avoid common test automation practice traps. 100% test coverage does not guarantee 100% coverage of all the customer use cases.

o Code Coverage: Measures the percentage of code executed by the automated tests. o Requirement Coverage: Ensures that all requirements are covered by test cases. o Functional Coverage: The extent to which the automated tests cover the functional requirements of the application. o Non-Functional Coverage: The extent to which the automated tests cover non-functional requirements (e.g., performance, security). o Automation Coverage: The percentage of test cases that are automated versus those that are manually tested.

Here are the most important automation metrics to monitor: 1. Test Coverage 2. Pass/Fail Rates 3. Test Execution Time 4. Test Maintenance Time 5. Defect Detection Rate 6. Flaky Test Rate 7. Test Execution Frequency 8. Test Coverage by Requirement 9. Bug Leakage 10. Cost Savings 11. Return on Investment (ROI) 12. Code Quality Metrics 13. Continuous Integration Metrics 14. Mean Time to Detection (MTTD) 15. Mean Time to Resolution (MTTR)

From my experience having good test documentation is crucial in highly changeable environment. It's far more easier to spend 5-10 minutes to update the documentation, than weeks or month later spend several hours trying to understand what was done, when and why.

o Return on Investment (ROI): The cost savings achieved through test automation. o Time to Detect Defects: The time from code check-in to defect detection. o Test Reusability: The extent to which test scripts can be reused across different test scenarios. o Script Effectiveness: The ability of test scripts to find defects.

Based on my experience, below are important: a) Test Coverage: Ensures significant portions of the application are tested, highlighting untested areas that may contain defects. b) Pass/Fail Rate: Provides a snapshot of application stability by showing the proportion of tests that pass versus those that fail. c) Test Execution Time: Aids in pinpointing performance issues and refining test suites, leading to more efficient resource utilization. d) Test Stability : Evaluates the consistency of test results across multiple runs, identifying flaky tests that can cause false positives or negatives. e) Environment Availability : Tracks the availability and reliability of the test environment to ensure tests can be executed without interruptions.

One thing I have found helpful is to start looking into the Test Data Management Metrics. Sometimes it is a challenge to ensure that the test data used in the testing process is adequate and accurate. 1. Test data coverage: Ensure you have covered all the possible values and fields for your system testing. 2. Test data quality: Make sure the data is accurate, consistent, current, and complete. 3. Test data reusability: Track those data sets used repetitively on multiple scenarios. 4. Test data preparation time: Consider Mocking and Data masking to reduce the prep time. 5. Test data volume: Measure the volume of data that can be handled by the application under test.