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

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 ...

The U.S.-based metrology equipment manufacturer has launched a non-contact photoluminescent imaging system to reveal defects and other non-uniformities in silicon, thin film, and III-V multijunction ...

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

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

The principle of Bug Clustering states that the majority of defects are often concentrated in a small number of modules or components of a software system. It can be seen as an application of the ...

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

The small and complicated features of TSVs give rise to different defect types. Defects can form during any of the TSV ...

Hiring risk studies consistently show that technology failures increase variability in hiring outcomes and raise compliance concerns. Adoption research also indicates that recruiting software often ...

Modern software teams are expected to ship faster every quarter. At the same time, the systems they ship become more complex, more distributed and more regulated. As systems scale from monoliths to ...

Abstract: Deep learning techniques for software defect prediction enhances the efficiency of defect detection in large-scale projects. However, the prediction outcomes often include a certain ...