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 ...

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 ...

The approach toward software testing has drastically changed over the years. It has changed from manual testing to automation frameworks and now to AI-based testing. It isn’t just about increasing ...

When evaluating AI for testing, prioritize approaches that keep teams in control and maintain end-to-end testing connectivity.

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 ...

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 ...

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 ...

For businesses seeking to deploy AI models in their operations — either for employees or customers to use — one of the most critical questions isn't even what model or what to use it for, but when ...

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 ...