Consumers today live in a mobile-first world. According to a study by App Annie, “In 2021, consumers spent a record 3.8 trillion hours on their phones and downloaded around 230 billion apps.”
Mobile dominance is also shaped by the fact that, on average, Americans are now spending less time watching TV and more time on their cell phones.
As we all spend more time on our devices, technology leaders are under pressure to deliver more and better native mobile experiences faster than ever. From banking to retail, healthcare to transportation, every industry recognizes that delivering mobile app experiences is vital to survival.
Technology leaders have a challenging task ahead of them when it comes to delivering these experiences – especially since app quality, security and business agility are benchmarks for success. Using native mobile test automation strategies as part of the development process can help ensure these needs are met and consumers are satisfied.
Below, we address some of the key trends driving the need for native mobile app testing and quality assurance (QA). We also examine why adding artificial intelligence (AI) to the testing approach can rapidly create next-gen mobile experiences for customers.
Trends driving the challenges for quality mobile app experiences
While there are numerous reasons and subjective circumstances that make quality assurance of native mobile apps more difficult than, say, web or desktop applications, the convergence of three trends adds a multiplier effect to the complexity of generating engaging mobile app experiences for consumers.
The wide world of mobile devices
Building a native mobile app has become a top priority for many businesses to attract customers. However, the explosion of different mobile devices used by customers to access native mobile apps is a huge challenge for QA and Agile software development teams. Not only do these teams need to account for new devices coming to market, but they also need to be able to scale their mobile testing practices across multiple device types to validate apps on every device customers use.
According to Statista, the number of mobile devices in use worldwide will reach almost 15 billion in 2021, up from just over 14 billion in the previous year. The number of mobile devices is expected to reach 18.22 billion by 2025, an increase of 4.2 billion devices compared to 2020. Each new generation of devices from Apple, Samsung, Google and several other original equipment manufacturers (OEMs) means test coverage is expanding must quickly and quickly adapt to market demand.
Additionally, each device is expected to differ in terms of device resolution/screen size, operating systems (and supported versions), screen orientations, scroll views, and other factors. More often than not, this leads to numerous development challenges that can slow deployment cycles — and worse, impact mobile app quality.
Last but not least, native mobile app testing is inherently more demanding than web application testing. Not only is setting up the required hardware expensive and cumbersome, the software is also usually more difficult to use.
Faster development cycles affect the scalability of mobile testing
The time to market to get new digital products, services and features into the hands of consumers is a competitive advantage. Ultimately, companies that ship more grow faster. However, QA and testing have created delays and bottlenecks for modern app development as the entire deployment lifecycle has been merged with newer development tools, making applications easier to build and deploy. Mobile app testing needs to scale in parallel to ensure faster deployment time.
Today there are many different approaches to scale test automation for native mobile applications. Options range from running locally with virtual devices (simulators/emulators) or real devices, to a local mobile grid/lab, to Docker containers/virtual machines or remote cloud testing services.
Testing native mobile applications is a challenging endeavor as there are many moving parts and many points of failure. For a successful implementation, everything must harmonize perfectly. For example, running a single Appium test involves the following:
An Appium server with all required dependencies installed. A mobile device or emulator/simulator. Valid test code logic. A compiled mobile application. Application web service APIs that are running and stable (if applicable). Not just “hoping for the best”
To scale tests to multiple devices for cross-device validation needs, prepare to introduce more failure points for each device tested. A test may run fine on one device, but fail on another for various unknown reasons. This can result in development and QA teams spending a lot of time investigating and debugging these errors to find the root cause.
Adding more devices to the mix means adding even more conditional logic to the test code to account for those devices and their inherently different characteristics (screen size, operating system, orientation, location devices, and other factors). All of this adds more coded logic to a test suite or framework to maintain and eventually refactor in the future as the app changes.
For the above reasons, companies often cannot afford to scale their mobile test coverage to different devices due to test maintenance, more test difficulties, longer test execution times, or direct access to different devices. “Hope for the best” generally doesn’t work in these situations and ultimately the app experience suffers, leading to customers opting out.
Brand = mobile experience
It’s not enough for companies to simply deliver mobile apps faster; Apps have to be visually and functionally perfect at all times. Because a company’s relationship with its customers is reflected in how the market perceives every aspect of its own brand experience, especially on mobile, from identity to positioning to UI/UX.
For example, let’s take a mobile app for a retail company. If the “Add to Cart” button isn’t working, or is hidden behind another button on certain screen sizes when the user tries to click, or the text is off-center or hard to read, this business could not only lose a sale, but many before the bug is fixed.
Worse, it could lose potential customers and brand ambassadors forever. This becomes even more critical when it comes to industries like healthcare, banking, and insurance, where functional and visual issues with an app can have severe consequences for end users that will not be tolerated.
If you don’t think that visual flaws, poor UI/UX experiences and other functional glitches on a mobile website or application can tarnish a brand’s reputation in seconds, consider the following statistics collected by uxcam.com:
88% of users are less likely to return to a website after a poor user experience. Mobile users are five times more likely to abandon a task when the site is not mobile-friendly. 80% of all internet users own a smartphone. 53% of mobile users leave websites in just three seconds. 90% of users have stopped using an app due to poor performance. Only 55% of organizations are currently conducting user experience testing.
And PWC found that 32% of customers would leave a brand they love after just one bad experience.
Why visual AI is needed to test and improve native mobile apps
Organizations are trying different approaches to address these challenges, including Shifting Left, where the development team takes more responsibility for testing and uses AI to speed up the testing process and achieve higher coverage.
But visual AI is the technology that will propel mobile apps into the next generation of customer experiences and help ensure brand loyalty. Software engineering leaders and development teams can leverage visual AI to better arm themselves for the growing challenges of mobile app testing through improved quality engineering tactics and strategies.
Without visual AI, the number of UI/UX permutations for a mobile app is overwhelming and impossible for development and QA teams to navigate. Fortunately, there is a new technological approach based on visual AI to asynchronously validate a native mobile application in parallel and easily on many different devices in a single test execution (as opposed to tens or hundreds).
This means that visual AI-powered native mobile testing can provide instant access and validation across a vast inventory of mobile devices with different screen sizes/viewports and operating systems. And because it’s asynchronous, teams don’t wait for the device to connect or for test results, allowing tests to run as quickly as possible.
The promise of visual AI
Today, visual AI-powered mobile testing technologies can outperform traditional in-house device test farms and traditional real-world test clouds. Tests that took 8 to 10 minutes are now completed in less than two minutes.
Development teams that need to quickly deliver high-quality mobile apps use visual AI-powered technology to reduce test execution time by up to 90%. Additionally, technology teams using these technologies do not require extensive training. Users can get started in minutes. Using advanced computer vision AI algorithms already built-in, they can run automated tests on simulated mobile devices in seconds. Teams using this technology report significantly higher test coverage than the benchmark and faster release speed.
Ultimately, knowing that visual and functional regressions can be instantly detected with visual AI across all mobile device variants gives reassurance to those responsible for ensuring that a mobile user experience is exactly how it was intended for the customer.
The end goal for any company tackling the challenges of mobile app delivery and brand experience is to future-proof their approach so that native mobile app testing can finally keep up with mobile app development. With visual AI, it’s now possible to continuously deliver mobile apps with a speed and accuracy unmatched by traditional mobile testing techniques.
Moshe Milman is co-founder and COO at Applitools.
data decision maker
Welcome to the VentureBeat community!
DataDecisionMakers is the place where experts, including technical staff, working with data can share data-related insights and innovations.
If you want to read about innovative ideas and up-to-date information, best practices and the future of data and data technology, visit us at DataDecisionMakers.
You might even consider contributing an article of your own!
Read more from DataDecisionMakers