Over the years, software testing has become a more integrative and automated process to test the quality, performance and usability of app development.

Different trends and tools have impacted app quality assurance testing, such as DevOps for agile development, but one of the most significant impacts has been the integration of AI tools for software testing.

Automated Test Scripts  

Firstly, AI enhances testing automation, which is now the preferred testing ground for quality assurance engineers instead of tedious manual processes, with few exceptions.

A great example of an AI application in the testing process is writing automated tests that can actively respond to a configuration change within the test environment.

Tests can then reconfigure themselves without any manual support. It means that failed tests will be able to reconfigure itself to run successfully.

Multiple Integrated Testing

There are many advantages to automated testing, but the most obvious is running more and different tests at once, as applications become more complex and testing focus more integrated than before.

Although testing can have a specific parameter for verification – loading, usability, security, performance – it’s more likely for cross-testing to happen, where multiple tests run together.

It makes sense to pair performance testing with usability testing to determine if the application runs the way it is supposed to be while being easy to use.

AI-driven Analytics

AI-driven analytics are becoming more and more important for interpreting test data. Because of the complexity of modern applications, interpreting test results manually is less feasible and too time-consuming in fast-paced DevOps environments.

AI can help testing engineers to further understand and use test data to benefit future app development.

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