3 Things to Consider When Applying Machine Learning & AI in Test Automation

Test automation is an exciting field of software testing. More than 20 years of automation testing innovations have transformed how development teams produce, release, and maintain software applications.

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For testers, automation means no more dealing with boring and repetitive tests. For developers, it means not having to wait very long for feedback and release status. For project managers, automation means no sub-par application sees the light of day. Development teams pursue test automation in the hopes that it can make their work easier.

Test automation is the latest trend in software quality assurance. The latest trend in test automation, on the other hand, is the application of machine learning and AI technologies.

The Need for ML and AI in Automated Testing

Are you ready to learn more about how machine learning and AI technologies aid test automation efforts? Let’s start by understanding why they’re needed in the first place.

Automated testing isn’t perfect. While it’s fast and reusable, it can never replace the human touch. It’s challenging to execute performance tests and UI/UX automated tests, though both aren’t impossible anymore. There’s also something known as the test automation trap.

The test automation trap is the cycle that QA engineers fall into when they’re just starting with test automation. It’s the cycle of keeping your automation suite updated while changes to the application are coming left, right, and center. One variable name change, and you’ll need to change every single instance of that variable name in every single test script.

Without AI and machine learning, it isn’t easy to ensure test stability in applications with dynamic elements. Non-ML test scripts cannot automatically adapt to changes in an application.

AI and ML in Test Automation Platforms

Test automation tutorials are great for when you’re starting. If you want to know more about development ecosystems and full-stack testing frameworks, you might want to read about automated testing for beginners. Tutorials like these not only help you find subjects to learn. They also help you find a test automation platform to use in your automated testing efforts.

Thankfully, test automation platforms nowadays use AI and ML technologies to answer one of test automation’s most glaring drawbacks: code maintenance. Most test automation platforms these days won’t require programming skills. 

3 Things to Consider When Applying ML/AI in Test Automation

Not all tests can be automated, but you can trust a good chunk of your testing efforts to automation. How can you incorporate machine learning and artificial intelligence into test automation and software development?

1. Pattern Recognition

Pattern recognition is what machine learning does best. And, there are definitely patterns in software testing cycles and test results. Making sense of these patterns in tests and results can help in continuous development and integration. These patterns open up opportunities for machine learning and AI in test automation.

However, machine learning may be limited in the sense that it can only learn and improve with human engineering. Knowing what knowledge to feed into any machine-learning algorithm is critical to its success. This is the training part of machine learning, and the benefits of the end result apply to testing and development as well.

Determining design patterns can help developers and QA testers apply efficient development and testing best practices specific to their development style. ML technologies can help find data patterns in test cycles. These patterns mark changes in specifications and provide insight as to how these changes actually affect test results.

2. Predictability and Self-Healing

Code maintenance is a real pain. And, automated test script developers should constantly strive to update test suites if they wish to continue reaping the benefits of test automation.

Fortunately for newer QA testers, test automation platforms are easy to come by nowadays. QA testers in the past had to develop automated tests and apply machine-learning algorithms on their own. Now, there are free, cloud-based, and open-source test automation platforms that harness AI and machine learning power.

Self-healing AI in test automation can detect changes in dynamic elements and apply changes to test scripts automatically, thereby enhancing test stability. Testers don’t even need to learn testing platforms and specific programming languages to make automated tests. Though, programming skills are still valuable skills for a tester to have.

Machine learning can be used to predict which tests can verify certain parts or specifications of an application. These predictions are quite helpful in agile and continuous delivery and integration teams. With machine learning and test data tools, it’s now possible for developers to validate branch coverage and predict the minimum number of tests required to achieve it.

3. Test Data Generation and Test Analytics

A simple application in development requires a multitude of tests to ensure stability, reliability, security, and functionality, among many others. Imagine how much test data is needed and how much testing is performed within organizations with tens or hundreds of concurrent projects. Using AI/ML in test analytics and data generation can be helpful for decision-makers.

Test data are created each time a test is executed. The sheer amount of test data can be overwhelming to navigate, let alone make sense of. 

Test analytics can help business owners, project managers, and other decision-makers quantify and mitigate business risks. They’re also helpful in finding potential business opportunities and understanding the overall impact of an application while it’s still in development.

With test automation platforms leveraging AI and machine learning technologies, predictions get smarter and, therefore, more reliable over time. 

Final Words

Though conventional test automation has proved beneficial, it wasn’t long before people saw that there were downsides. Conventional test automation demanded at least intermediate programming skill levels. They were unstable, and they required knowledge of specific testing frameworks.

Test automation reached new heights with the integration of AI capabilities and machine learning technologies. They effectively eliminated the technicalities required to learn and start automation testing.

The integration of AI and ML in test automation is fairly new. But, nobody can deny their immense and valuable impact on software testing. They have the potential to transform that part of the software development life cycle.

ML and AI aren’t just buzzwords you hear and use to attract people into trying out test automation. The future of automation (and many other technologies, for that matter) relies heavily on developments in AI and machine learning.

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