“AIOps” stands for “artificial intelligence in IT operations,” or using machine learning and data science to solve IT problems. AI can help with many IT functions, including detecting and remediating outages, monitoring availability and performance, and IT service management. Like with DevOps, a tester plays an important part with AIOps—they just have to determine what that is.

Testers gather lots of metrics about defect count, test case execution classification, and test velocity—but this information doesn't necessarily answer questions around product quality or how much money test efforts have saved. Testers can better deliver business value by combining test automation with regression analysis, and using visual analytics tools to process the data and see what patterns emerge.

The advantages of shifting left and testing as early as possible are obvious. But as you automate more testing, the test suite grows larger and larger, and it takes longer and longer to run. Instead, just automate the process of finding the right set of tests to run. The key to that is machine learning. This isn't AI bots finding bugs autonomously without creating tests; this is a different way to use machine learning, and it’s far simpler.

A move to DevOps creates an opportunity to shift the testing process to the left. But what if you went further? DevOps supports continuous testing, so you can advocate for a constant focus on quality, with testing permeating the entire software development process. Here's how you can actually have a faster testing process when the software is tested throughout the lifecycle, by developers, testers, and automation alike.