A common problem for QA leaders is to assume that machine learning can replace all manual testing. This can overwhelm the system with too much data and diminish its performance.

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

The Evolution of AI in Software Testing: From Machine Learning to Agentic AI By Mike Wager, Contributor Published 02-26-25 Submitted by Keysight Technologies Photo by Mauro Sbicego on Unsplash ...

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 is an ...

The same machine learning system can be used to optimize test suites’ execution order such that the suites arrive at the first errors quicker; this can save resources of either time or physical ...

Our understanding of progress in machine learning has been colored by flawed testing data. The 10 most cited AI data sets are riddled with label errors, according to a new study out of MIT, and it ...