It’s no secret that major advances in AI have started to revolutionize the ways that just about every industry does business. With the release of OpenAi’s generative AI success GPT-4, businesses across the globe are evaluating how AI can help solve their challenges. In the mobile app space, developers are leaning into AI to solve the many problems and headaches device fragmentation introduces. Can the combination of AI and device fragmentation solve this problem for good?
In this article, we discuss the what happens when you combine the two.
Understanding device fragmentation
Dealing with device fragmentation continues to be one of the main sources of frustration among mobile app developers. How do you effectively manage the user experience for a mobile app available on hundreds of devices? Over the last few years, the device landscape has grown significantly.
According to Android, there are over 24,000 unique android devices created across 1,300 brands. And that’s just one operating system.
Fragmentation isn’t limited to the device itself. It also impacts the version, operating systems, screen size, and a variety of other factors. Consider these factors:
Screen sizes and resolutions
Mobile devices come in a range of screen sizes and resolutions, from small smartphones to large tablets. This fragmentation necessitates developing apps that can adapt to different screen sizes and resolutions to provide a consistent user experience.
Operating systems (OS)
The mobile market is divided into different operating systems, such as iOS, Android, Windows Phone, and more. Each OS has its own unique features, design guidelines, and programming languages. Within each operating system, there are multiple versions in use simultaneously. Some may be using the latest version, while others are on older versions. Developers should consider the compatibility of their app with different OS versions.
Hardware capabilities
Mobile devices differ in terms of hardware capabilities, such as processor power, memory, camera quality, sensors, and connectivity options.
Device manufacturers
Different manufacturers produce mobile devices, each with their own specifications and customizations. These variations can affect app performance and compatibility.
Network conditions
Mobile devices can access the internet through various network connections, including Wi-Fi, 3G, 4G, and 5G. Since each network type has its own characteristics and bandwidth limitations, developers must account for network conditions while designing and testing their apps.
What is AI doing about device fragmentation?
AI is being used to dampen the effects of fragmentation.
Responsive Design and AI
Mobile apps are being developed more intelligently. AI algorithms can analyze screen size, form-factor, and user behavior to adjust design elements dynamically. Developers can leverage responsive design datasets to write better performing software. Figma, a design and prototyping tool, offers a host of AI plugins that allow teams to build responsive app designs quickly.
Adobe XD includes an AI-powered feature called “Responsive Resize” that automatically adjusts design elements when the screen size or layout changes. This feature analyzes the design and intelligently predicts how different UI elements should adapt to various screen sizes and resolutions.
Predictive Analytics
With AI and machine learning algorithms, programs can analyze how users are interacting with applications. They can analyze device and usage data to predict trends and patterns associated with different devices. With this information, AI can help developers make data-driven decisions when prioritizing device support and optimizing app features.
A recent article in Forbes outlined:
By using AI, you may receive aspects of analytics that you may not have considered otherwise. AI can find the information that is harder to pick out and turn it into comprehensive analytics. These analytics can help your team locate weak or troubled areas of your software that may have been missed otherwise.
Cross-platform development tools
AI-powered frameworks and tools streamline the development process across multiple platforms. By automating the conversion of code and UI elements across platforms, AI can help reduce the effort required to address device fragmentation and ensure consistent app performance and appearance across different operating systems.
AI, fragmentation, and mobile device testing
Fragmentation makes it challenging for developers to synthesize a seamless experience across devices. It is also especially hard on mobile app testing teams – who have to ensure tests perform as expected on different devices.
Increased fragmentation has made mobile app testing more challenging. AI has come to solve some of the issues.
Test automation makes it easier to quickly execute test cases across multiple devices. From code-based frameworks to no-code engines, there are many mobile test automation tools available. With AI and machine learning advancements, automating test cases is even easier.
Automation test case creation
AI tools can create test automation from natural language descriptions, quickly spin up mocks and stubs for external services. AI can even analyze requirements, or text based test cases and automatically generate test cases.
Recommended test cases
Some testing platforms can recommend new test cases or suggest changes to existing test cases based on new input, user interaction, and by analyzing code changes.
Chatbots
Sofy just released SofyBot, an AI chatbot that helps testers create test cases, debug failure, and analyze results. You can even provide SofyBot a JIRA link, and watch it generate manual test steps for you.
Error prediction
AI can analyze code and can determine if the code is optimal, or leaves itself open to failure. AI-powered test automation tools can even perform gap analysis and recommend steps and actions to improve code coverage.
Reporting and analytics
With modern test automation tools, AI reveals valuable insights and reporting. Tools like SofySense give you fine-grained access to debug logs and test performance data.
Other ways to combat fragmentation
While fragmentation leaves its own unique challenges for mobile app testers, AI can alleviate some of those pain-points. As device fragmentation increases (which it will), modern AI-powered testing tools will need to adapt to meet new use cases.
Testers have more ways to help with fragmentation. Testing on real devices is one of those ways. The problem many testers face is they’re not able to access a real device, so they may rely on emulators or simulators.
Unfortunately, emulation and simulation aren’t entirely accurate, and can misrepresent results.
The good news?
You don’t have to manage your own device lab to benefit from real device testing. In fact, you don’t even have to have the device in your possession. With a real device lab, you can access real devices hosted on the cloud.
For example, with Sofy’s device lab, you can choose from dozens of real devices and record a manual test. Each time you acquire a real device, you’re provisioned a clean instance with your application already installed. Once you record your manual steps, you can automate your test with a single click. All of this is done through Sofy’s no-code engine.
AI and device fragment should go hand-in-hand
AI fragmentation leaves its own unique challenges for mobile developers and testers. By leveraging AI and real-device testing, hopefully you’ll be able to navigate them.
Disclaimer: The views and opinions expressed above are those of the contributor. They do not necessarily represent or reflect the official beliefs or positions of Sofy.
