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.

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 ...

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 ...

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

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 ...

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

In the field of machine learning, researchers tend to think that the method known as deep learning makes its best predictions when models are trained on a lot of data, like hundreds of thousands ...

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

It is critical to highlight that machine learning is not a panacea and should be augmented with traditional performance testing and monitoring approaches to achieve the best outcomes.