AI can speed up testing, but if you trust it too much, you might ship bugs faster than ever — with no one clearly accountable.

Testing places unique demands on AI. Errors carry real business risk, and fragile tests or slow updates can quickly erode trust in results. As a result, while momentum around AI in testing is strong, ...

Value stream management involves people in the organization to examine workflows and other processes to ensure they are deriving the maximum value from their efforts while eliminating waste — of ...

Real-world deployments show 40% test cycle efficiency improvement, 50% faster regression testing, and 36% infrastructure cost savings.

The cost of not upping software quality assurance will be evident not only in the marketplace but on a company’s bottom line and in the lives of people.

Calsoft introduced an AI-powered approach to Test Impact Analysis that eliminates unnecessary test executions in CI/CD ...

When LambdaTest was founded, the problem it set out to solve was far more contained but with the rise of AI-generated code ...

In healthcare software, quality is inseparable from compliance. A feature working as designed is not enough. Every workflow, integration, and data exchange must protect Protected...Read More The post ...

AI-generated test cases have significantly accelerated software testing workflows, but refining outputs often requires manual edits or restarting the generation process. TestMu AI’s latest release ...

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 ...

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 ...