Applications must be usable on a wide variety of devices, browsers and platforms. To test these capabilities, QA professionals typically use cross-browser testing and cross-platform testing, in which artificial intelligence and machine learning have been integrated.

Cross-platform testing involves testing browsers, operating systems, and gadgets to confirm that the software works properly. Cross-platform testing ensures customers can use the software on any device and any operating system. Cross-browser testing involves many of the same aspects, but generally relates to web applications. These QA practices are especially important for mobile apps. After all, mobile apps should be accessible through phones and tablets, and on various platforms such as Android and iOS. Likewise, web applications may need to be compatible with multiple browsers, devices, and platforms – operating systems, hardware, etc.

The proliferation and increased adoption of AI and machine learning (ML) have made new capabilities available to QA professionals, including those tasked with cross-browser testing and cross-platform testing. platforms. However, these advances are not without drawbacks and complications.

Why and how to perform cross-platform testing

Why. Cross-platform testing plays a vital role in the software quality assurance process. The practice makes it possible to evaluate the behavior of an application or a website in several contexts. Cross-platform testing uncovers issues that vary from platform to platform, such as user interface, usability, consistency, and performance issues.

For example, an application may work fine in the Chrome browser, but its user interface may look distorted when rendered in, for example, Microsoft Edge or Mozilla Firefox. Similarly, an application may work fine on Windows, but not on Linux.

How? ‘Or’ What. With cross-platform testing, it’s especially important to have use cases ready in advance. Also, it is crucial to know the likely end-user device types and platforms.

Second, QA professionals must select the platform on which to run the test. Test results will likely vary depending on the platform used. As such, they should identify a frequently used platform that is well known to their team to get the best results.

The role AI can play in cross-platform testing

AI and ML help organizations eliminate testing gaps by increasing people’s skills and abilities to analyze data in real time. Additionally, AI/ML are valuable technologies if software scalability and quality are high priorities.

A move to automation saves time on testing while not introducing a lot of errors. And intelligent automation, such as AI/ML, can deliver significant business benefits, including revenue generation, speed of execution, 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 the time and effort required to test a particular scenario.

Benefits of integrating AI into cross-platform testing

When you integrate AI into the software testing process, several benefits emerge.

Improved accuracy. Even the most experienced testers can make mistakes when manually testing software. But automated testing can come to the rescue. AI can enable testers to develop more efficient testing methodologies, produce more efficient test scripts, and discover new ways to deliver the best possible UX. Technology can make testing easier by improving accuracy and speed.

Faster test creation. AI can help improve the performance of test recorders. These smart test recorders are able to create better test scripts much faster. AI tools for software testing can help record user sessions and determine missing tests in a path. If no test is found, these tools can add one to the test library.

Faster time to market. Running the tests manually is time-consuming and tedious, especially when a development team often changes the source code of the program. AI can run and rerun automated tests at a much faster pace and at lower costs, allowing applications to get to market sooner.

Complications of integrating AI into cross-platform testing

AI and ML can offer many advantages to testers, but there are also disadvantages.

Identification of test data. Adopters must train their AI/ML models before they can use the technologies in a live production environment. Typically, a data scientist performs model testing and training rather than a software test engineer. The tester should understand how the data from the model evaluation 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 datasets.

Required Skills. When integrating AI into the software testing process, having trained software testers is essential. Specifically, testers who understand how to interact with the given system and run tests when AI is integrated into the testing process. Testing these systems can be as tricky as the new technologies themselves. This QA requires considerable technical and data science skills on the part of 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 application testing. The goal of AI in software QA is to make testing smarter and more efficient, including cross-browser and cross-platform testing.

AI in software testing can reduce time-consuming manual testing, allowing human testers to focus 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 need to prepare for the future. They should be able to adapt test automation to reduce testing costs and be ready to use new advanced technology approaches in the years to come. IT professionals, especially in QA, should expect AI to start replacing entire technology stacks for automated testing.

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