QA teams now use machine learning to analyze past test data and code changes to predict which tests will fail before they run. The technology examines patterns from previous test runs, code commits, ...

The software testing landscape is undergoing a seismic shift. For years, continuous automation testing (CAT) platforms have been the gold standard for reducing manual testing and ensuring ...

Companies investing in generative AI find that testing and quality assurance are two of the most critical areas for improvement. Here are four strategies for testing LLMs embedded in generative AI ...

While thorough testing and quality assurance checks have always been important steps in the software release and updating processes, they’re taking on a new and higher significance in an ...

Everywhere you turn, someone is talking about AI — AI this, AI that. No wonder some people roll their eyes at the mention of artificial intelligence. For some, it’s all smoke and mirrors, just a ...

The performance of software greatly influences a company's workflow, efficiency, and financial success. High-quality software smooths out processes, boosts productivity, and gives businesses an edge ...

In some ways, data and its quality can seem strange to people used to assessing the quality of software. There’s often no observable behaviour to check and little in the way of structure to help you ...

From generating test cases and transforming test data to accelerating planning and improving developer communication, AI is having a profound impact on software testing. The integration of artificial ...