AI-powered but not autonomous
yet: How artificial intelligence can
address three common UI testing
challenges
What do Teslas and test automation have in common?
They’re both being revolutionized by artificial intelligence.
Teslas are not fully autonomous – they still require a human
behind the wheel to understand and deal with road
conditions – but they have used AI to improve the user
experience, reduce accidents, and improve efficiency. The
same is true for AI-powered automated software testing.
For test automation, AI can augment and accelerate test
authoring, test maintenance, and risk management, freeing
developers’ and testers’ time to focus on the strategic work
that will ultimately help them create better code and
release higher quality software.
Read on to learn how AI can be used to eliminate common
testing headaches, as well as what the future of autonomous
testing holds.
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Why UI testing is so challenging today
The stakes are high for today’s test engineering teams. As more functionality gets shifted to the front end, testing in-app workflows becomes increasingly complex. Meanwhile, customers have both a greater reliance on digital experiences – and higher expectations for them. Recent research shows that 60% of users abandon online purchases due to poor user experiences – and 42% decide whether to stay on or leave a website within 10 seconds. Meanwhile, more functionality has shifted to the front end, making testing in-app workflows increasingly complex.
Here’s why it’s so challenging:
Test authoring is slow
Authoring an effective, stable end-to-end test takes nearly as much time as developing the feature being tested. This increases the overall time spent on quality assurance, and in turn lengthens the time to market.
Test maintenance is a headache
A recent survey of software developers found that 59% claimed to deal with flaky tests on a monthly, weekly, or daily basis. That’s because each time the UI changes, test scripts must be updated to reflect those changes, or they will fail. This makes test maintenance a tedious and highly- repetitive task. Modern development teams want to integrate their testing into their CI/CD processes and continuously test on each software build, adding more pressure on test engineers to prevent flaky tests from failing builds.
Talent is scarce
In today’s development environment, it’s easier to find full-stack developers than automation engineers. Coupled with the fact that the typical developer doesn’t like to spend a lot of time on testing activities and manual testers find coding challenging, we have a gap in skill sets needed for effective in-app workflow end-to-end testing.
How AI can address common UI testing challenges
Imagine a fully autonomous testing environment where tests are authored, maintained, and executed automatically. We’re not quite there yet, but there are some exciting ways AI can make it possible to speed up both test authoring and maintenance.
Most test engineering teams today use script-based automation tools, which pose major challenges for test maintenance, test resilience, and reusability. These require frequent updating as the application evolves and produce an overwhelming number of false positives, script errors and bloated test suites.
These frameworks are often open-source and include Selenium, Cypress, Puppeteer, and others. They look at the HTML Document Object Model (DOM) to identify visual elements, typically by using a single CSS selector or XPath to identify a visual element. However, when that CSS attribute changes or the location on the web app page changes, the test breaks. These coded tests are also slow to author, often taking up to eight hours to write a single test.
Some of these frameworks have evolved to include a recorder that helps capture flows faster. They can then be converted to coded scripts or replayed as a recording. Unfortunately, these recorded tests rely on the same unstable, single-selector approach to finding elements. These frameworks also require unique skills to understand the framework’s capabilities and syntax. This skills dependency often shifts the burden of testing onto a few individuals and means that test coverage is often low.
Tools like Tricentis Testim have added AI to the recorders to capture the elements with stable, multi-attribute locators that can find the elements even when several attributes change. This capability enables test engineers to record and configure a test in minutes with no coding required – reducing test authoring time by 95% over scripted testing tools and up to 50% compared to other low-code record/playback tools.
Here’s how it works: Testim inspects the entire web application HTML DOM to identify hundreds of attributes that define each element. If the color, text, or location of an element changes, Testim can identify it and keep the test valid – both accelerating test authoring and eliminating the challenge of flaky test scripts.
A future of fully autonomous testing?
Like the self-driving car, full autonomous testing is not yet a reality. In theory, autonomous testing would be capable of learning the context of the app under test, then authoring and executing the tests, requiring no human intervention.
The next evolution in autonomous testing could enable automatic test authoring by connecting to staging or production environments and observing the way humans interact with the app— either by the QA/dev or by real users in production. At this level, AI could learn common user flows over time, determine the most repeated actions, cluster these actions into groups, and convert them into reusable user scenarios.
A core advantage here would be AI’s ability to analyze complex product analytics data to identify and anticipate user needs – by watching user interactions, understanding the paths that users take, and making sure that those use cases always work as expected.
But what about the quality engineers who are doing this work today?
As AI eases the burden on test engineers, they will be able to use their expertise alongside developers to build high-quality code from the ground up – instead of spending their days on test maintenance.
Tricentis Testim: AI-powered, automated UI testing
Tricentis Testim is the fastest way to create your most resilient UI tests for customer facing web and mobile applications. Testim’s AI-powered self-healing and auto-improving Smart Locators keep your tests stable and minimize maintenance. Execute parallel, cross-browser tests on Testim’s grid or on third-party grids. Testim also integrates with your dev tools so you can stay in your workflow to create tests, branch tests, collaborate, and trigger test runs on CI builds. Troubleshooting is simplified by aggregating error types, providing relevant data, and showing before/after screenshots. Agile teams scale efficiently with tools to help them control and manage the growth in tests and teams.