With machine learning, we can reduce maintenance efforts and improve the quality of products. It can be used in various stages of the software testing life-cycle, including bug management, which ...

In this article, let’s explore how machine learning is revolutionizing software testing and breaking new ground for QA teams and enterprises alike, as well as how to successfully implement it.

To determine the causal effect of a decision or tool, companies routinely use A/B testing: comparing outcomes reveals whether ...

Software testing, especially in large scale projects, is a time intensive process. The article explores optimizing test execution, saving machine resources, and reducing feedback time to developers.

I then looked a step further and researched testing capabilities based on data, analytics, and machine learning that development teams and QA test automation engineers can leverage to develop and ...

Machine learning’s impact on technology is significant, but it’s crucial to acknowledge the common issues of insufficient training and testing data.

Where real data is unethical, unavailable, or doesn’t exist, synthetic data sets can provide the needed quantity and variety.

To better select patients for adjuvant therapy, it is important to accurately predict patients at risk for recurrence. Our objective was to train, validate, and test models of EC recurrence using ...

A new blood test pioneered by diagnostics company AOA Dx (AOA) can detect ovarian cancer in symptomatic women with high ...

Researchers developed and tested a machine learning-based clinical decision support system to predict antibiotic resistance.