“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.
As your QA team grows, manual testing can lose the ability to focus on likely problem areas and instead turn into an inefficient checkbox process. Using machine learning can bring back the insights of a small team of experienced testers. By defining certain scenarios, machine learning can determine the probability that a change has a serious defect, so you can evaluate risk and know where to focus your efforts.
With 2020 upon us, software development firms seeking to increase their agility are focusing more and more on aligning their testing approach with agile principles. Let’s look at seven of the key agile testing trends that will impact organizations most this year.
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.
Raj Subramanian, developer evangelist at Testim.io, talks about artificial intelligence; the differences between AI, machine learning, and deep learning; and what AI means for the future of software testing.
In this interview, Geoff Meyer, a test architect in the Dell EMC infrastructure solutions group, discusses whether or not testers should be nervous about artificial intelligence, what testers can do right now to keep up with the times, and when AI is most useful for software teams.
In this interview, Daria Mehra, the director of quality engineering at Quid, explains how people can use machine learning to better contextualize data, details the complexity of test automation and how to be sure you have enough test coverage, and defines the term “artisanal testing.”
In this interview, Jason Arbon, the CEO of Appdiff, explains how artificial intelligence is going to change the way we test our software. He talks about why testers shouldn't be afraid that AI will take their jobs and shows how machine learning can actually be approachable.