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.

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

So how exactly does machine learning affect the world of software development and testing, and what does the future of these interactions look like?

Machine Learning Changing Software Testing One of the first ways we've seen machine learning (ML) being used in testing is to make the current automated tests more resilient and brittle.

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 ...

Launchable, which emerged from stealth mode on Thursday, is developing a SaaS product that uses machine learning to predict the likelihood of a failure for each test case, given a change in the source ...

StormForge provides machine learning-based, cloud-native application testing and performance optimization software that helps organizations optimize application performance in Kubernetes.

Amazon has been testing a machine learning software in space that can analyze Earth observation images on its own and send only the best ones to Earth.

A machine learning “red team,” known as the Test and Evaluation Group, will probe pretrained models for weaknesses. Another cybersecurity team examines AI code and data for hidden vulnerabilities.

AI and ML are transforming advanced materials testing, enhancing accuracy and efficiency in predicting material properties and behaviors.