Calsoft executive reveals AI-driven testing approach cutting software release cycles by 70%

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

Harnessing AI and Machine Learning for Advanced Materials Testing

Materials testing is critical in product development and manufacturing across various industries. It ensures that products can withstand tough conditions in their ...
The Southern Maryland Chronicle on MSN

How are QA teams using machine learning to predict test failures in real time?

QA teams now use machine learning to analyze past test data and code changes to predict which tests will fail before they run. The technology examines patterns from previous test runs, code commits, ...
InfoQ

Testing Machine Learning: Insight and Experience from Using Simulators to Test Trained ...

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...
The Register on MSN

Machine learning could yield faster, cheaper lithium-ion battery development

Researchers claim model can cut years from testing cycles Scientists have developed a machine learning method that could ...
The Victoria Advocate

New SEI Tool Enhances Machine Learning Model Test and Evaluation

PITTSBURGH, Oct. 17, 2024 /PRNewswire/ -- Software systems with a machine learning (ML) component often fail in production. One reason is that ML models are frequently developed in isolation, making ...
SiliconANGLE

NetSPI bridges security gaps with new ML/AI pentesting solution

Cybersecurity services and software provider NetSPI LLC today announced a new machine learning and artificial intelligence penetration testing solution to bring a more holistic and proactive approach ...
datanami.com

New SEI Tool Enhances Machine Learning Model Test and Evaluation

PITTSBURGH, Oct. 18, 2024 — Software systems with a machine learning (ML) component often fail in production. One reason is that ML models are frequently developed in isolation, making it impossible ...