Discover how AI and machine learning reduce flaky tests, cut maintenance costs, and improve accuracy in modern automated testing.

Machine learning systems operate in a data-driven programming domain where their behaviour depends on the data used for training and testing. This unique characteristic underscores the importance of ...

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.

When testing machine learning systems, we must apply existing test processes and methods differently. Machine Learning applications consist of a few lines of code, with complex networks of ...

Let’s review how machine learning has developed, and then explore how ML techniques are radically changing the software testing industry.

Princeton researchers applied machine learning methods to develop an optimal policy for ordering common blood tests in a hospital’s intensive care unit. From left: Computer science graduate student ...

For cost and capacity reasons in high volume production, test of difficult-to-measure parameters needs to be eliminated. In these situations, it is common ...

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