Applications need to be usable on a wide variety of devices, browsers and platforms. To test such capabilities, QA professionals typically use cross-browser testing and cross-platform testing, into which artificial intelligence and machine learning have been integrated.
Cross-platform testing is the practice of testing across browsers, OSes and gadgets to confirm that the software works correctly. Cross-platform testing guarantees that clients can use the software on any device and any OS. Cross-browser testing involves many of the same aspects, but generally pertains to web applications. These QA practices are especially important for mobile applications. After all, mobile apps need to be accessible through phones and tablets, and on various platforms such as Android and iOS. Similarly, web applications may need to be compatible with multiple browsers, devices and platforms — OSes, hardware, etc.
The proliferation and increased adoption of AI and machine learning (ML) has made new capabilities available to QA professionals, including those tasked with cross-browser testing and cross-platform testing. However, these advances don’t come without drawbacks or complications.
Why and how to perform cross-platform testing
Why. Cross-platform testing plays an integral role in the software QA process. The practice helps assess how an application or website behaves in multiple contexts. Cross-platform testing helps uncover problems that vary across platforms, such as UI, usability, consistency and performance concerns.
For example, an application might work fine in the Chrome browser, but its UI might look distorted when it’s rendered in, say, Microsoft Edge or Mozilla Firefox. Similarly, an application might work fine on Windows, but not on Linux.
How. With cross-platform testing, it is especially important to have the use cases ready upfront. In addition, it is crucial to know end users’ likely types of devices and platforms.
Secondly, QA professionals need to select the platform on which to run the test. The test results will likely vary based on the platform in use. As such, they should identify a frequently used platform that’s well known by their team to garner the best results.
The role AI can play in cross-platform testing
AI and ML help organizations eliminate testing gaps by augmenting people’s skills and capacities to analyze real-time data. In addition, AI/ML are valuable technologies if scalability and software quality are high priorities.
A transition to automation saves testing time while not introducing many errors. And intelligent automation, such as AI/ML, can provide significant business advantages, including revenue creation, execution speed, accuracy, compliance and cost savings.
AI can help create test scripts, improve test analysis and eliminate the need for routine maintenance. By using AI in cross-platform testing, QA professionals can reduce human error and save on the time and effort needed to test a particular scenario.
Benefits of integrating AI into cross-platform testing
When you incorporate AI into the software testing process, several advantages emerge.
Improved accuracy. Even the most skilled testers can make mistakes when manually testing software. But automated testing can come to the rescue. AI can enable testers to develop more effective testing methodologies, produce more effective test scripts and discover new ways to deliver the best possible UX. The technology can make testing easier by improving accuracy and speed.
Faster test creation. AI can help improve the performance of test recorders. Such intelligent test recorders are adept at creating better test scripts much faster. AI tools for software testing can help record user sessions and determine the missing tests in a path. If no test is found, such tools can add one to the test library.
Faster time to market. It is time-consuming and cumbersome to execute tests manually, particularly when a dev team often changes the program source code. AI can run and rerun automated tests at a much faster pace, and at reduced costs, allowing applications to hit the market earlier.
Complications of integrating AI into cross-platform testing
AI and ML can provide many advantages to testers, but downsides exist too.
Identifying test data. Adopters must train their AI/ML models before they can use the technologies in a live production environment. Typically, it’s a data scientist who performs model testing and training rather than a software test engineer. The tester should understand how the data in the model assessment process was obtained, specified and used. If the training data is inaccurate, the results will be incorrect. It is therefore crucial to create high-quality, coverage-oriented test data sets.
Required skills. When integrating AI into the software testing process, it is critical to have trained software testers. Specifically, testers who understand how to interact with the given system and run tests when AI is integrated into the testing process. The testing of these systems can be as tricky as the new technologies themselves. This QA necessitates considerable technical and data science skills from the testers, which distinguishes the position of an AI tester from that of a manual or automated tester.
The future of test automation
One of the recent trends in AI is to improve current tools and frameworks to address the challenges faced in testing applications. The goal of AI in software QA is to make testing more intelligent and efficient, including for cross-browser and cross-platform testing.
AI in software testing can reduce time-consuming manual testing, allowing human testers to concentrate more on complicated tasks. For testers, AI can help develop better testing methods, write better scripts and discover new ways to deliver the best possible UX.
AI is already changing how testing works. As processes evolve, QA professionals should prepare themselves for the future. They should be able to scale test automation to cut testing costs and be ready to use new, advanced technological approaches in the years to come. IT, particularly QA, professionals should expect AI to begin replacing whole technological stacks for automated testing.