As AI technology and no-code automation tools continue to evolve, manual testing seems to be losing its edge. This perception may or may not align with reality, as multiple factors are impacting ...

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, ...

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 ...

Leapwork recently released new research showing that while confidence in AI-driven software testing is growing rapidly, accuracy, stability, and ongoing manual effort remain decisive factors in how ...

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

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 ...

Historically, software testing in India relied heavily on manual processes and automation frameworks that required extensive coding expertise. These methods often slowed development cycles and limited ...