Mar 29, 2023
Mobile App Performance Testing With AI
However, the idea of using automation tools for improved test speed creates a need for further evolution of the test process. And here comes the role of Artificial Intelligence!
With that being said, let us quickly walk through the detail on how Artificial intelligence has transformed mobile performance testing and the idea of test automation and what potential it holds for the future of software testing.
AI For Improved Mobile Performance Testing Automation
Testing a mobile application require testers to overcome challenges related to user experience, screen size variability, network compatibility, battery life usage, and many more benchmarks that can hamper the ultimate product performance.
Besides, the use of AI in performance testing of applications is still a process under construction. On one hand, we have advanced solutions like image recognition and NLP (natural language processing), while on the other hand, we still lack autonomy.
Nevertheless, there are many mobile test automation tools that are using AI to complement test creation, run test analysis, and improve overall test maintenance. Here are a few ways AI could help advance the entire process of mobile performance testing:
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Codeless Test Automation
Traditional mobile testing tools require testers to write automation code on their own. It comes as a challenge for manual testers as not always have enough technical expertise to work on scripts. Besides, writing tests is consuming and adds to the cost of the project. Therefore, testers prefer to rely on codeless testing and now turning towards test automation with AI.
These are the tools that have ‘record & playback’ options to record user flow. It means a manual tester working on a test process records the flow which is further imitated by the tool. However, the process comes out to be high-maintenance and consuming as it does not contain any possibilities to update recordings and work on slight changes in the flow.
Tools with AI or artificial intelligence offer advanced record and playback options that not only simplify test maintenance but even work on NLP to define test cases. Ultimately, it allows simplified updates and maintenance.
A quick example of the tools that could be used to work on AI-powered codeless test automation include Testsigma, Accelq, and Test.ai.
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Element Location
Conventionally, the mobile testing tools worked on selectors to determine what elements must be interacted. However, these selectors were very delicate to handle due to their tendency to change for evolving app code. Ultimately, they were likely to add to maintenance burden.
However, the contemporary solutions to application testing with AI offers provision for visual locators that eliminate the need for Selectors and add more robustness to the test process. Besides, they perform better than hard-coded selectors by working on the visuals appearance of the elements allowing the process to work for any sort of app evolution.
Some of the quick examples of such tools can be listed as Test.ai and Katalon Studio which are using visual AI algorithms and visual object recognition to simplify the process.
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Self-healing Test Cases
Such issues usually occur because of the underlying app structure to test functionalities that calls for false positives. This usually involves flaky identifiers or operating systems that may lead to flawed testing.
However, the AI based test tools come with self-healing abilities that could detect changes made to an element or flow in context to the predefined test steps. Ultimately, AI allows tests to run smooth and without any human intervention. A quick example of tool with such capabilities is QMetry that not only identifies a problem but analyze it to propose a relevant solution.
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Visual Validation
In visual testing, testers work on evaluating the interface of an app to attain the expected output. The process involves identifying visual inconsistencies, the colors, the position of the buttons, etc. And to take the process further, traditional testing works on the app’s DOM to understand the presence and status of visual elements. Also, the test process requires writing thousands of assertion code lines based on browsers, screen sizes, operating systems, etc. All in all, the entire process is highly inefficient to pursue with complicated maintenance.
On the other hand, AI-based visual validation tools allow testers to identify all the visual inconsistencies using the snapshots of the current and previous screen with effective regression. These mobile testing tools are made to locate elements in rendered screens and compare them using computer vision to meet the expected goals. At present the feature is supported by tools like Kobiton and Applitools.Looking At The Future: How AI Could Further Complement Mobile Testing?
Though AI technologies are constantly making space for innovation in the mobile testing segment, the evolution of the technology itself holds a lot in regard to the future of the quality assurance industry.
Intelligent Gap Analysis
Gap analysis comes as an opportunity to software testing companies as well as enterprises with digital goals. It allows identification of untested code for an application which is crucial for modern apps with complex flows where testers are likely to miss testing some parts of the code.
Though it can be tolerated to some extent within the test environment, the app maker should never take a chance on when a user might start to interact with some new flows. Besides, most of the time, the untested part of the code is very likely to contain the bugs or flaws.
At present, testers working on gap analysis run a combination of static analysis and dynamic analysis of all the code changes and the current test cycle. However, the process often ends up to be very consuming.
The use of Artificial Intelligence for gap analysis could allow testers to learn how users are interacting with an application. For instance, it can keep a watch for users taking on an unexplored journey on the application and could alert the developers and testers on such gaps. Besides, gap analysis with AI can even be used to optimize resource allocation by locating the areas of focus depending on intensity of usage.
Automated Test Generation
All the existing mobile test automation tools only enable testers to automatically execute test cases, which is not enough to meet the rapidly growing technology needs of the market. And therefore, automating test case execution alone do not satisfy the time and cost objectives surrounding the complex app development lifecycles. Though some tools have made it easier for testers to focus on the UI part and improve the app flow, they are still very far from the concepts like Unit and API testing.Here, Artificial Intelligence could be harnessed to foster unit testing and API test generation allowing developers and testers to run tests more effectively with innovation.
To Sum Up!
For any organization that is aiming to renovate their development or testing strategy, AI-based tools can prove to be the key to succeed on advanced testing practices like agile, CI/CD, and more. Especially, when it is evident that AI is likely to rule the industry by simplifying the most complex tasks related to testing.
Also, adopting the AI tools for test automation requires business organizations and development companies to upgrade on their CI/CD practices with mobile-specific CI/CD strategies. At BugRaptors, we understand every dynamic and detail of AI-based mobile testing and with the objective to redefine the quality engineering goals, we are all set to take our expertise to the world.
Contact today to learn how we can complement your AI-based mobile testing journey.
