As you’ve no doubt heard, artificial intelligence (or AI, as it’s more commonly known) and machine learning (ML) technologies have already begun reshaping countless industries. These dynamic solutions have now started making their way to the mobile app testing space. Let’s talk about AI and machine learning for app testing.
Conceptually, by strategically integrating technologies like AI and ML into mobile app automated testing tools, your team can further elevate its DevOps capabilities, maximize efficiency, and create higher-quality applications. But how possible is that today?
Test automation technology is great, so why modify it with AI?
If your team leverages low or no-code automated testing tools, you know just how powerful these solutions have become.
In years past, your team would have had to waste untold hours writing testing protocols and customizing them for each round of app evaluations. The development of no-code or low-code automation tools literally changed the game for DevOps teams.
Today, your crew can effortlessly create and run tests with a few clicks. Automating these protocols allows you to run more extensive app testing, uncover bugs or glitches, and deliver a better product to your target audience.
Yet while there’s a lot to love about automated mobile app testing tools, there’s also plenty of room for improvement.
For instance, automated testing configurations are typically app-specific. Additionally, many automated testing solutions are unable to assess the quality of user experience factors, meaning quite a bit of manual testing is still necessary.
And this is where AI and machine learning in app testing comes in: Artificial intelligence and machine learning technologies can help remove the necessity for the remaining repetitive tasks.
ML and AI eliminate test-generation redundancy, saving developers even more time and reducing DevOps expenses. By reducing the high cost of entry associated with mobile app development, these tools can potentially make it possible for businesses of all sizes to launch their own applications without the need for spending hours performing very similar test tasks again and again.
Considering AI and machine learning in app testing
Artificial intelligence and machine learning solutions are already supporting mobile app testing efforts. These technologies have been adopted on a broader scale as well. While the application of these innovations is still quite new, according to recent data, 35% of companies already use AI to automate business processes and promote efficiency. Small businesses aren’t far behind: One out of four say they leverage at least one form of AI technology.
We’re now in an era where tech entities of all sizes have embraced ML and AI. Let’s take a look at a few ways that AI can potentially improve the process.
Expedited element location
Traditionally, testing tools rely on selectors to determine which app elements they need to interact with. Unfortunately, selectors can be inadvertently altered as the code changes. When this happens, more of your tests will fail, and you’ll have to go back to the drawing board to determine why.
By comparison, AI and ML-powered mobile testing technology can leverage locators instead of fragile selectors.
The locators are not only more effective at targeting app elements but also resistant to changes, as they identify elements by analyzing their visual attributes. AI-driven locators can ensure that your tests will function properly, even if selectors change.
Self-healing capabilities
False positives are a persistent headache associated with many test automation platforms.
When they run rampant, your DevOps team has to check and recheck app elements to determine whether a particular functionality has gone haywire or the test simply provided incorrect results. There go any time savings you enjoyed by automating your tests.
Leading automated mobile app testing tools use AI to “self-heal.” Put another way, they can automatically determine when an element locator was changed and make the necessary model adjustments to prevent false positives.
Self-healing capabilities have been a major goal in the mobile app testing space for a long time now. Self-healing capabilities are currently only being used in a limited fashion. However, as ML and AI technologies become increasingly robust, testing platforms will likely gain the ability to self-heal all testing protocols.
Thanks to machine learning innovations, it’s reasonable to expect that platforms will become more efficient at healing over time. As this happens, they’ll learn which types of code updates are most likely to change element locators and become more efficient at remedying those issues.
Visual function validation
Visual function validation, alternatively referred to as visual testing, allows developers to verify that the appearance of an application aligns with its intended design. During visual function validation, testers will ensure that all elements are in the right positions and shaded in the appropriate colors, among other things.
Traditionally, testers have had to examine the underlying structure of applications to determine the presence, quality, and status of their constituent elements. If you’ve ever conducted visual testing this way, you know it’s extremely tedious and inefficient.
That’s changing. For example, Sofy’s visual testing capabilities make this a lot easier, and AI-powered testing tools can additionally incorporate a visual validation feature that rapidly identifies discrepancies by comparing current screen layouts with baseline snapshots of various app screens.
These tools can perform these comparisons during every round of regression testing to confirm that no elements were accidentally altered when your team pushed out coding changes, reducing the need to visually identify these factors yourself.
Optimized scriptless test automation
Whether your team has already gone scriptless or plans to do so soon, you’ll want to make sure your platform includes AI and ML technologies. After all, advances we’re seeing in AI today are ultimately just another step in the No-Code Revolution.
Scriptless testing tools without AI capabilities typically rely on the “record and playback” method. The tester must manually record a flow; the tool then imitates their actions to test the app’s functionalities.
Anytime the flow changes, the tester has to record a new test flow. As you’re well aware, flows change frequently during the course of an app development project. The inevitable result is tons of wasted time and energy.
Top mobile test automation tools ditch the record and playback method in favor of ML and AI. They use natural language processing (NLP) technology to convert a tester’s plain statements into functioning tests.
What’s next for AI and machine learning app testing?
Machine learning and artificial intelligence technologies are already revolutionizing how DevOps teams conduct mobile app testing. However, there’s plenty more on the horizon. Here are a few predictions a little further into the future:
Intelligent gap analysis
In the probably-not-too-distant future, AI and ML technologies will enable mobile testing tools to learn how users interact with your mobile app.
For instance, these tools may determine that quite a few people are taking a user journey you haven’t tested. When the platform detects these trends, it will alert you so you can address the gap in your testing protocols.
By automating your intelligent gap analysis capabilities, you can more effectively allocate testing resources. For instance, you can focus new testing efforts on usage frequency and prioritize the features most important to your consumers.
Automated test generation
Currently, mobile testing tools only automate test execution, not creation. AI and ML technologies can change all that.
As these technologies continue to advance, developers hope to automate the generation of user interface (UI) tests. Especially ambitious developers are also exploring ways to automate API and unit testing, which still largely rely on manual processes.
Ready to embrace the future of automated app testing?
The combination of AI and machine learning app testing is here: Sofy recently launched Sofy Co-Pilot, powered by OpenAI’s GPT technology. Co-Pilot acts as a personal assistant, answering questions and processing data at your requests. Experience the benefits for yourself. Get a demo today to try it out for yourself.
Disclaimer: The views and opinions expressed above are those of the contributor and do not necessarily represent or reflect the official beliefs or positions of Sofy.
