Anthropic's latest AI model has found more than 500 previously unknown high-severity security flaws in open-source libraries ...

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

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, ...

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 ...

Is quality engineering (QE) an extension of software testing or a complete change in responsibilities? That's a question I want to answer today. As COO of an enterprise test execution cloud platform, ...

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 ...

We’re at the beginning of a new era in quality engineering, one shaped by agentic AI. While generative AI has captured global attention, the real transformation in software testing is only just ...

Software testing is an essential component in ensuring the reliability and efficiency of modern software systems. In recent years, evolutionary algorithms have emerged as a robust framework for ...