David Dang, DevOps Automation expert, discusses how to leverage AI and machine learning for quality assurance that will make your automation suite more robust.
“AI and machine learning… What is it and what can it do for your company? More and more companies are moving toward AI and machine learning for more efficient and effective software quality.“
Machine Learning – is the ability to go through a large set of data and find patterns within that data to then teach the machine how to recognize those patterns and the actions that should be carried out with that data. But, how do we teach the machine to automatically recognize those patterns? Artificial Intelligence. Once we have identified and taught the machine what to do with those patterns, AI can then be used to automatically recognize and interact with those patterns.
For instance, say you have a fairly large automation suite with thousands and thousands of test cases. When running those test cases, it reported a lot of failures related to various issues. By digging into your analytics/data you can identify the patterns of what your users are doing and key in on those specific/high importance test cases. Essentially, by matching what the user is doing with your test cases, there is no need to run a bunch of tests that have low impact or low importance to what your users are utilizing from the application. In addition to telling the machine to look for a specific pattern of a user, you can leverage AI to make predictions on how to react to future scenarios based on past interactions. AI has the capabilities to automatically run tests based on the interactions it encounters, such as, which tests should be run, how often to run, etc.
By leveraging AI and machine learning for quality, your automation suite will become much more robust and your will save time by not having to test things that don’t have a large impact on your users.
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Read our blog, A Case for Quality… We Don’t Need No Stinkin’ QA, by Andrea Galicia.