A Guide to Test Metrics for Functional & Automation Testing

Introduction

Testing is considered the crucial aspect of the software development lifecycle (SDLC). This is where developers validate their code to ensure it meets all requirements and functions correctly across various scenarios. Nevertheless, testing is a process that is tailored to individual needs. It includes various methods, like functional testing and test automation, each presenting unique challenges and goals.

To ensure that the program operates as expected by its specifications is functional testing. It verifies that the program meets what it was designed for rather than caring about how it operates. By using tools and scripts to execute tests automatically, test automation, on the other hand, might increase productivity.

Functional Testing Metrics

Validating the software against the functional requirements listed in the project documentation is the main goal of functional testing. Making sure every component of the program functions as planned is the aim. The following are some crucial indicators that determine how well functional testing is going:

1. Test Case Execution Rate: The ratio of completed test cases to all test cases scheduled is displayed by this indicator. A high execution rate suggests that the testing procedure is proceeding as planned, However, a low pace may indicate upcoming delays or a lack of resources.

Formula: (Number of Test Cases Executed / Total Number of Test Cases) * 100
Why It Matters: Teams can monitor the success of their testing endeavors by utilizing the test case execution rate. While a low execution rate could be a symptom of delays or bottlenecks, a high execution rate shows that testing is going according to schedule. Teams can modify their testing plans and resources by keeping an eye on this measure, ensuring that all important tests are completed before release.

2. Defect Detection Rate: The rate of defect detection is the percentage of problems discovered during functional testing relative to the completed test cases.

Formula: (Number of Defects Found / Total Number of Tests Conducted) *100
Why It Matters: This measure is essential for assessing how well your test cases work. While a low rate can mean that your test cases are not sufficiently covering the functionality or edge cases, a high defect detection rate indicates that your tests are comprehensive and capable of identifying problems. Teams can improve their test cases and focus on parts of the application that need additional testing by reviewing this approach.

3. Requirement Coverage: The number of requirements that have been tested is recorded by requirement coverage, a specific category of test coverage.

Formula: (The Number of Requirement Tested / Total Number of Requirements) *100
Why It Matters: This measure guarantees that every functional requirement has undergone testing and been validated. It's crucial to make sure the program serves the intended goal and that all required features function as intended. Low requirement coverage may be a sign of testing process flaws that are being overlooked, potentially resulting in problems.

4. Test Coverage: An index known as "test coverage" measures how much testing has been done on the software. It can be divided into various fields, including functional coverage, code coverage, and requirements coverage.

Formula: (The Number of Requirements Tested / Total Number of Requirements) *100
Why It Matters: A high-test coverage level reduces the chance that issues will be missed and ensures the bulk of the application becomes tested. It's important to remember that having 100% test coverage cannot ensure that the software is error-free. Rather, it indicates that every requirement or code path that has been specified has been tested. In addition to working for significant coverage, teams must consider the quality and usefulness of their test cases.

5. Defect Density: A metric called defect density counts the number of defects discovered in a software unit, such as 1,000 lines of code or a function.

Formula: (The Number of Defects / Size of Software Components) * 100
Why It Matters: Teams may focus their testing efforts where they are most required by using defect density to assist them in identifying parts of the software that are more prone to problems. Teams can evaluate the effects of their testing and development procedures and make data-driven choices to raise the standard of their software by monitoring this statistic over time.

Test Automation Metrics

Test automation is necessary for increasing testing effectiveness and providing regular test coverage, particularly in environments that allow agile development and continuous integration/continuous delivery (CI/CD). Some important measures of success that determine the effectiveness of test automation projects are as follows:

1. Automation Coverage: Automation coverage is the amount of automated test cases compared to all test cases. It provides you with a clear picture of the amount of your testing that takes place automatically versus manually.

Formula: (The Number of Automated Test Cases / Total Number of Test Cases) * 100
Why It Matters: When the automation scope is high, a significant part of the testing process is automated, which can shorten the test, eliminate manual work, and make the test more reliable. However, because not all tests offer easy automation, it is essential to find a balance between the two types of testing.

2. Execution Time: Test execution time measures the total time taken to execute automated tests. This metric is crucial for understanding the efficiency of the automation suite.

Why It Matters: Increasing or maintaining test coverage while cutting down on test execution time can greatly accelerate the development process. To fix issues faster and reduce the overall time to market, developers can receive feedback more quickly when tests are executed in less time.

3. Test Maintenance Effort: The test maintenance effort indicates the time and resources required to update and maintain automated test scripts every time an application changes.

Why It Matters: One challenge with test automation is keeping up with the test scripts as the application improves. Automation builds could be balanced by a high maintenance effort. Teams can use this statistic to evaluate the maintainability of an automation framework and identify areas for improvement to save maintenance costs.

4. Defect Detection Effectiveness: This test evaluates how well-automated tests can identify errors. It is calculated as the proportion of problems discovered by automated testing. High effectiveness shows that automation has contributed significant improvements to the process of quality assurance overall.

5. Return on Investment (ROI): The financial return on the investment made in testing being automated is measured by test automation ROI. It measures the savings from automation with the one-time and regular costs of setting up and maintaining the automation suite.

Formula: (Cost of Saving from Automation – Cost of Automation / Cost of Automation) * 100
Why It Matters: When teams know the return on investment (ROI) related to test automation, they may find it easy to justify the cost of automation tools and resources. A good ROI shows that the automation efforts are giving off, whereas a negative ROI can suggest that the costs of automation surpass the benefits. These statistics are important for long-term planning and strategic decision-making.

6. Pass/Fail Rate: The percentage of automated test cases that pass compared to those that fail is shown by the pass/fail rate, which monitors the success rate.

Formula: (The Number of Passed Test Cases / Total Number of Automated Test Cases) * 100
Why It Matters: A high success rate shows that the application is stable, whereas a high failure rate may refer to problems with the test scripts or possibly the program directly. Teams may keep updated on the effectiveness of their automated tests and the stability of the application by tracking this statistic over time.

Conclusion

Ensuring that your software meets its quality objectives requires measuring testing success. By leveraging software testing services, teams can identify areas for improvement, provide crucial insights into the effectiveness of their testing efforts, and make data-driven decisions that enhance the overall quality of the product.

Targeting key outcomes in functional testing and test automation ensures that testing activities align with project goals, considering the current state of testing. As the software development field evolves, the efficient and effective production of high-quality software will rely on maintaining appropriate parameters.

About Author

Set the baby steps in 2019 as a trainee in manual testing, Shubham Pardhe has now become an experienced QA executive in Pixel QA.

His professional goal is to become an expert in test management tools.

Automation Testing