Allocating capital to autonomous security platforms outperforms traditional consultant-driven validation models.

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

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, ...

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

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

China’s push toward large-scale automation is entering a new phase as a major robotics player rolls out a high-profile test ...

Quality engineering must evolve faster than code; otherwise, agentic AI will move quickly, learn rapidly and fail expensively.

Overview: AutoOps extends DevOps by embedding AI across coding, testing, deployment, monitoring, and optimization to create ...

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.