Issues with test scripts and framework account for 40% of issues that DevOps teams face, according to a recent study. Here’s how machine learning can help.
Organizations that implement continuous testing within Agile and DevOps execute a large variety of testing types multiple times a day. With each test execution, the amount of test data that’s being created grows significantly, making the decision-making process harder.
With AI and machine learning, executives should be able to better slice and dice test data, understand trends and patterns, quantify business risks, and make decisions faster and continuously. Without the help of AI or machine learning, the work is error-prone, manual and sometimes impossible.
What you’ll learn:
- How machine learning helps make sense of extremely high quantities of test data
- Use machine learning to make actionable decisions around quality for releases
- How machine learning can enhance test stability over time
