Top 8 Key Metrics for Measuring Test Automation Success

Nowadays, to maintain software quality in the fast-paced Agile development environment, many companies have invested in adopting test automation during their development process.

Nevertheless, the costs of enterprise software testing will include different sources: engineering effort, license fees, and labour hours. Are the outcomes worth the price? How can we be so sure?

Most enterprise software testing teams spent significant time creating and configuring the framework, as well as incorporating the tests into their CI/CD pipeline and scheduling them to run periodically based on checked-in code. Then they determine that this marks the completion of the full automation testing cycle but forget one more crucial component of test automation solutions: assessing the effectiveness of the automated tests.

What I often see at testing projects is that teams are fully aware of the work they put into a project, yet the exhaustion of concluding it prevents them from thinking about its outcomes.

Let’s explore some test management metrics businesses can apply to measure test automation success effectively.

Why do we need Automation Testing Metrics?

DevOps and TestOps management are dependent upon test automation metrics. As it’s difficult to effectively manage a dynamic DevOps environment without a comprehensive understanding of what is occurring at a granular level, the appropriate metrics must be monitored. Additionally, tracking test automation data benefit businesses in sourcing and acquiring test automation tools.

Here are reasons that you should analyze test execution metrics at the beginning:

• Real-time Visibility: test management metrics will provide accurate insights regarding the software testing process, including the number of test cases in progress, error discovery, and automation execution time.

• Automation Effectiveness: performance metrics can be used to evaluate whether the automation testing can improve your app quality or if your team is just spinning its wheel. Moreover, it also indicates where testers should concentrate more during the development process to improve the testing effectiveness.

• Boosted Time to Market: test metrics will help you evaluate test duration and, from that, have an appropriate strategy to speed up the process.

How to Choose the Right Testing Metric?

The appropriate set of automation testing metrics varies depending on the business and team’s objectives. However, it’s crucial to choose a metric that calculates the automation value, especially on your first time implementing an automated testing approach. Some key considerations when selecting metrics for automation testing include:

• Connecting with corporate objectives: Any automation statistics utilized by the company must reflect the primary goals that it is striving for. For instance, a business that focuses on delivering software on time may consider using parameters that help identify test progress to be matched against success criteria.

• Improving the QA team’s performance incrementally: The chosen metrics should enable the organization to filter out ineffectiveness and improve over time. To evaluate progress, the metric should be based on an established baseline.

• Providing value to business strategy: A metric that you select should be useful in mapping the company’s overall strategy. It should not only provide a baseline signal but also encourage the team to develop a plan and work on it to ensure testing efficiency.

8 Key Test Automation Metrics for Measuring Success

There’s a plethora of metrics that provide you with a deeper understanding of the QA system, the team’s testing skills, and processes to identify and fix issues while boosting your team’s productivity. The following list of commonly-used metrics will help you improve the automation testing process:

1. Percentage of Automatable Test Cases

At the beginning of enterprise software testing, businesses can measure the percentage of test cases that are automatable concerning the total number of test cases in a suite. This parameter is used to determine which processes are prioritizing automation and which still demand human validations.

It’s helpful in formulating the appropriate enterprise testing strategy and generating a balance between automated and manual testing. Assist software development teams in assessing the feasibility of automating their testing process and estimating the time and resources required for test automation solutions.

The percentage of automatable test cases may vary depending on the type of application, its complexity, and the testing requirements. Typically, functional test cases that involve repetitive tasks, data-driven testing, and UI testing are more amenable to automation than non-functional tests such as performance, security, and compatibility testing.

Test metric calculation example:

Let’s say we have a total of 300 test cases for a software application. Out of these, 240 test cases are automatable using automated testing tools.

Automatable Test Cases % = (# of test cases automatable / # of total test cases) * 100

                                                            = (240 / 300) * 100

                                                            = 80%

2. Automation Test Coverage

Test coverage is a black-box approach that tracks the number of test cases performed. In automated testing, this metric illustrates the percentage of automation coverage compared to those done manually. This helps you track whether the QA team is meeting the automated test coverage goal or vice versa, increasing each sprint duration.

Test automation coverage metrics should be used in conjunction with other software testing metrics such as defect density and test pass rate, and the effectiveness of automated QA testing efforts can be impacted by a variety of factors, such as the quality of test scripts, the stability of the application or system being tested, etc.

For example, Visual Testing, often known as visual UI testing, ensures that the software’s user interface (UI) displays appropriately to all its end-users. Due to the lack of a test management platform to track the process to any degree of fidelity, it has been conducted using manual approaches. 

Now, the process can be automated easily using Katalon’s AI-Driven Visual Testing. The tool allows modifying the scope of visual tests with 3 comparison methods: content, layout, and pixel. Furthermore, it enables you to run in parallel with functional tests to cover both end-to-end scenarios and visual regressions.

Test metric calculation example:

Let’s consider a software testing project where 80 defects are considered acceptable based on the project requirements. During automated QA testing, the automation scripts successfully detect 45 of these defects.

Automation Test Coverage % = (# of automated tests / # of total tests) *100

                                                                  = (45 / 80) * 100

                                                                  = 75%

3. Automation Progress

This metric refers to the number of automated test cases that have been executed at a given time to track how well you’re doing in response to the objective of automated software testing.

Through this parameter, you can identify whether there are any significant deviations during the automation testing process. Reasons for these deviations include tasks being put on hold due to higher priorities, unforeseen elements in the software, or the inability to execute tester efforts effectively. It can be used to track the progress of an organization’s automated testing efforts over time and identify areas where additional automation is needed to achieve better testing efficiency and effectiveness.

Test metric calculation example:

Let’s say we have identified a total of 500 test cases that are automatable for a software project. Out of these, 300 test cases have been successfully automated.
Automation Progress % = (# of actual test cases automated / # of tests cases automatable) * 100

                                                      = (300 / 500) * 100

                                                      = 60%

Explore more: Digital Process Automation

4. Defect Density 

These KPIs measure total known issues, bugs, and errors discovered in software or other parts over the period of a development cycle. To understand whether your automation is running high-value test cases – look at the number of defects. It is extremely vital in Software Development Life Cycle (SDLC).

The number of defects encountered enables you to decide if the software is ready to be released. As this metric concentrates on the number of flaws detected, it allows QA engineers to identify the weak parts of software that require rigorous testing. The high parameter also reveals that the developing phase has coding difficulties and needs more resources or training.

Test metric calculation example:

Let’s assume we have a software system with a total size of 100,000 lines of code (LOC). During testing, we identify and document 150 defects in the system.

Defect Density = (# of known defects / total size of system) *100

                                   = (150 / 100,000) * 100

                                   = 0.15%

5. Automation Script Effectiveness 

Regression testing is often the focus of this measure. It evaluates defects found through practical automation testing compared to the overall number of acceptable defects in a project’s test management system.

It’s beneficial in figuring out what kinds of flaws the scripts can’t find and how different testing environments, such as integration and staging, can impact the script’s efficiency. For instance, you might anticipate lower effectiveness in your integration environment as scripting for new features hasn’t been completed. Similarly, in your staging environment, where you generally concentrate on regression testing, you can expect automation to detect the bulk of the bugs.

Test metric calculation example:

Let’s consider a software testing project where 80 defects are considered acceptable based on the project requirements. During automated testing, the automation scripts successfully detect 60 of these defects.

Automation Script Effectiveness = (# of defects detected by automation / # of acceptable defects) * 100

                                                                          = (60 / 80) * 100
                                                                          = 75%

Read more: How Automated Regression Testing Improves BFSI Software Quality and Efficiency

6. In-sprint Automation

Do you keep track of how much automation has been accomplished throughout the sprint? And what percentage is then chosen by the automation testing team members? This metric will assist you in understanding how close you are to in-sprint automation and improve the quality of several root causes.

This comprises the quality of backlog grooming, automation framework design, and stability of use cases in the sprint. To be most effective, the QA team should aim to automate new work in the iteration when the work is finished rather than in a subsequent sprint.

Test metric calculation example:

Let’s say during a sprint, a development team creates 20 automation scripts. After the sprint ends, they create an additional 10 automation scripts.

In-Sprint Automation % = (# of scripts created in-sprint / # of scripts created post-sprint) * 100

                                                      = (20 / 10) * 100
                                                      = 200%

7. Build Stability

If automation testing has been integrated into a CI/CD pipeline, this parameter can be leveraged to measure the tests’ reliability and consistency by calculating the percentage of shattered builds to stable ones.

This is an important metric because software builds are the foundation of the development process, and if builds are unstable, they can cause significant delays, errors, and frustration for the development team.

Enterprise software testing for robustness and scalability will help determine whether a stable build can be deployed to production. Improving build stability can help teams reduce delays and errors, improve developer productivity, and ultimately deliver higher-quality software products.

Test metric calculation example:

Suppose we have a project with 20 builds over a certain period. Out of these, 4 builds have failed.

Build Stability % = (# of build failures / # of builds) * 100

                                      = (4 / 20) * 100
                                      = 20%

8. Automated Test Pass Percentage

This more straightforward metric measures how many automation tests have passed. In automation testing, it’s used to understand the stability of your automation suite as well as its effectiveness. Having a low failure rate indicates that the script logic is correct, while a low pass rate requires more time spent validating failures. In case there are false failures, that is an early warning sign that your automation suite isn’t trustworthy.

However, the disadvantage of this metric is that it cannot show the quality of those tests. For instance, a test might pass as it only checks an unimportant condition or due to an error in the test code, while the software itself is not working as desired.

Test metric calculation example:

Let’s consider a scenario where a test suite consisting of 150 test cases is executed using automated testing. Out of these, 130 test cases pass successfully.

Automated Test Pass Rate % = (# of cases that passed / # of test cases executed) * 100

                                                        = (130 / 150) * 100
                                                        = 86.67%

On a Closing Note

Since many enterprises have set up comprehensive automation test suites to expedite their testing process, choosing the right automation testing tools and considering useful testing metrics are worth considering. The positive rate in such metrics shows that your enterprise software testing progress is on the right track, which can be the consequence of having appropriate skills among team members and the effectiveness of testing scripts and frameworks.

The faster enterprise software testing process requires more thorough testing metrics, which help not only the QA team identify bugs promptly but also the development team to fix bugs and have appropriate adjustments, resulting in more qualified apps.

In KMS Solutions, we concentrate on providing the best testing process by having skilful QA engineers in the team and choosing the appropriate metrics to deliver the qualified software in a timely manner.