Automation has long been vital to efficient mobile app and game testing strategies. However – it’s impossible to ignore particularly in the last few years how the infusion of AI has enhanced the capabilities of testing automation in several ways. Not only is this leading to much faster testing cycles but is also delivering far more accurate and predictive insights, directly improving the quality and reliability of mobile applications.
Having said this, there’s a lot of hype around AI in many different industries now, and it’s sometimes difficult to understand exactly how the integration of AI technologies within testing tools and frameworks leads to greater efficiency. In this article we’re going to do a deep dive into this extremely current and in-demand topic, showing you exactly how AI technologies could transform your testing strategies.
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The Rise of AI and Automation in Mobile Testing
It’s common for people to think of AI as some kind of magic black box that just does whatever you ask it to, but this isn’t how it works. An AI model is made up of different building blocks – large language processing (LLP), machine learning, OCR (optical character recognition), etc – and all of these things need big data in order for them to be trained, iterated over and improved.
Traditional automation in mobile testing has revolved around frameworks such an Appium and Selenium, and these have become fundamental in optimising and streamlining the testing process. Their ability to automate repetitive tasks and ensure consistency across devices and platforms has proved crucial for maintaining quality and consistency. We’re huge proponents of the benefits of automation when testing mobile apps and games, and we see first-hand how automation helps in executing complex test cases consistently, which in turn reduces the possibility of human error and accelerates the development cycle.
Integrating AI into mobile app testing is a game-changer in terms of how it can enhance the capabilities of traditional automation. Here’s how:
Predictive analytics
AI can enhance testing frameworks by introducing features such as predictive analytics, which can forecast potential future failures based on historical data. This enables you to pre-emptively fix problems that you didn’t even know you had – further reducing the cycle time for debugging further down the road.
Automated test case generation
Imagine an AI creating test cases for you without you having to think about it based on what it thinks it needs to look at. Of course, this doesn’t replace the need for you to consider what you need to test, but it’s highly possible that the AI will suggest things and find things you didn’t even consider. Automatic creation of test cases based on predefined parameters and patterns identified through AI can be a real time-saver
Machine learning and adaptation
AI algorithms can analyse outcomes from previous test executions, learning to recognise patterns and adjusting testing strategies accordingly.
Dynamic adaption
AI dynamically adjusts testing parameters in real-time, adapting to changes in the app’s behaviour or in response to external factors such as operating system updates or new device releases.
But where can you find this kind of AI that’s baked into a testing tool without you having to spend development time integrating it into your testing? A unique aspect of AI in automation is demonstrated by T-Plan Robot’s approach to image-based automation testing. This method leverages AI to recognise and interpret visual elements of an application exactly as a human tester would.
Our technology captures screenshots during test execution to verify that each screen displays correctly on different devices. This is particularly effective when visual elements are critical to user interactions, such as in apps with heavy graphical content, or dynamic interfaces. This is just one example of how we can leverage the power of AI.
Practical Applications of AI in Mobile Testing
Artificial Intelligence is reshaping mobile app testing, offering advanced solutions that go beyond traditional methods. But what about the specific practical applications that AI can assist you with? Here’s a look at how AI is being effectively applied to mobile app automation testing:
Visual Testing
We’ve already touched on this – T-Plan integrates AI to perform visual checks, ensuring the app’s UI appears as expected across various devices. We do this through our advanced image detection algorithm – something that no other tool on the market can do, and certainly is something that you would have great difficulty replicating trying to build your own test automation framework using an open-source library such as Appium.
Performance Optimisation.
AI can simulate different network conditions and loads to see how the app behaves, which is crucial for apps running on diverse mobile networks and devices. It’s sometimes easy to fall into the trap of assuming everyone is on an extremely fast 4G connection all the time – and this just isn’t true in many parts of the world. AI can help simulate different types of network connections and conditions to see how your app might be optimised for better performance, for example, under a simulated 3G connection.
Anomaly Detection
AI models are great at recognising patterns and can detect anomalies in the application’s performance and behaviour during testing phases. This is very important for mobile applications, where unexpected behaviours can significantly affect the user experience.
Challenges and Considerations
While the integration of AI into mobile app testing offer considerable advantages there are, of course, still challenges that arise from using these technologies.
Integrating AI tools with existing testing frameworks and CI/CD pipelines can make the setup process more complicated. Compatibility between new AI tools and existing software’s require significant customisation and configuration to ensure the tools seamlessly communicated with one another.
As well as this, effective leveraging of AI and machine learning in mobile app testing requires specialised skills that may not be present in every testing team which in turn can create a barrier to entry for many organisations. Training existing staff or hiring new talent can be costly and time-consuming but it is crucial for the successful adoption of AI testing technologies.
Although AI can significantly reduce the need for manual testing – thus cutting costs – the initial setup, operation, and maintenance can be quite expensive. The upfront investment not only includes the tools themselves but also the infrastructure required to support them. As technologies evolve ongoing updates and potential upgrades can add to the cost making it a considerable long-term investment.
Curious to learn how AI can supercharge your automated app or game testing? Request a free demo of T-Plan using the link below.
Potential Future Trends
As technology continues to evolve the landscape of mobile app testing is also set to undergo significant changes. The future of mobile testing requires us to adapt to new technologies and enhance capabilities to meet emerging challenges. We’ve listed some potential future trends that could shape the next phase of mobile app testing:
5G Impact
The roll out of 5G technology is poised to transform mobile app testing by introducing much higher data speeds and significantly reduced latency. The need to develop new testing strategies specifically designed to handle the enhanced capabilities of 5G will be imperative. Testers will need to ensure that mobile applications can perform optimally under the new network conditions, managing larger volumes of data and maintaining performance at lower latency levels.
Increasing Security Focus
With the rise of cybersecurity threats, AI tools are increasingly being used to conduct security assessments and compliance testing. These tools can efficiently scan code for vulnerabilities, simulate attack scenarios, and validate security protocols to ensure your mobile app is fortified against potential security breaches.
AR and VR Testing
AI could play a crucial role in testing augmented reality (AR) and virtual reality (VR) as they become more prevalent in mobile apps. This would be done by simulating real-world user interactions and environmental conditions. By creating virtual rest environments that mimic how users would interact with AR and VR features, ensuring applications deliver a seamless and immersive user experience.
Voice and Sound Analysis
Another integral part of mobile applications that we have seen increasing over the years are voice controlled interfaces. Therefor AI’s ability to automatically test voice and sound functionalities could crucial. This includes ensuring that voice commands are accurately recognised and appropriately responded to across various languages and accents. AI will streamline the process of testing these functionalities by automating voice inputs and analysing the responses for accuracy and timeliness.
Additional Resources
- AI in Testing: What it is, and Why it Matters | TechBeacon
- AI in App Testing – How to Use it Effectively | Medium
- Leveraging AI for Mobile App Testing: Pros, Cons, and Optimization | Sofy.ai Blog
- Survey Sees AI Playing Larger Role in Test Automation | DevOps.com
- Webinar: The Future of Mobile App Testing with AI and Automation | TechWell
- Book: “Experiences of Test Automation: Case Studies of Software Test Automation” by Dorothy Graham and Mark Fewster
Frequently Asked Questions About the Rise of AI and Automation in Testing
How does the integration of AI into mobile testing frameworks impact the accuracy and reliability of automated accessibility testing?
We can very significantly enhance the precision and reliability of automated accessibility testing by enabling thorough identification of issues like colour contrast and text size, and by simulating diverse user interactions. This ensures mobile apps comply with global accessibility standards, significantly improving usability for those with disabilities.
What are the specific challenges and solutions for implementing AI-driven stress testing on mobile applications under varying network conditions?
Challenges can include accurately simulating real-world network fluctuations and training AI models to predict app behaviour under such conditions. To solve this, sophisticated simulation tools are needed to mimic diverse network environments and employ advanced machine learning techniques to refine predicative models based on comprehensive training and validation using real usage data.
Can AI-driven testing frameworks adapt to the rapidly evolving mobile OS ecosystem, and how can they manage continuous updates without human intervention?
AI-driven testing frameworks are exceptionally well-equipped to adapt by using machine learning models that automatically update testing parameters with each new OS release and patch. This capability ensures that mobile apps continue to function efficiently across all new OS versions without requiring frequent manual updates to the testing scripts.
