Poor software quality cost the U.S. economy an estimated $2.41 trillion annually in 2022, according to the Consortium for ...

A recent SD Times Live! Supercast shed light on practical solutions to stabilize the testing environment for dynamic AI applications.

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

Transition from reactive quality assurance to proactive quality engineering by embedding shared responsibility throughout the ...

Test automation has become a cornerstone of modern software development. As release cycles accelerate and user expectations rise, quality assurance teams must deliver reliable results at speed. Among ...

Identify sources of unnecessary cognitive load and apply strategies to focus on meaningful analysis and exploration.

Autonomous AI testing agents and hyper-automation are rapidly becoming go-to solutions in quality assurance, writes MANDLA MBONAMBI, CEO of Africonology.

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

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

Test environments don’t fail because teams lack discipline or automation. They fail because dependent systems evolve faster ...