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How to Use AI/ML Systems to Revolutionize Testing and Test Automation

How to Use AI/ML Systems to Revolutionize Testing and Test Automation

Software testing and test automation is a technology that has certainly come a long way in the past few years and is set to go even further. Artificial intelligence is now more capable than ever to help automate the testing process and to help companies revolutionize their testing forever.

With the help of machine learning, or pattern recognition technology that is designed to identify patterns by using algorithms to predict trends that may occur in the future, artificial intelligence is now more beneficial than ever when it comes to testing and test automation. This pattern of recognition allows the machine to primarily learn and better predict what is going to happen and then apply it to new data.

How Can AI be Used in Test Automation?

A great way to determine how useful this type of technology might be is to look at a few possible scenarios in which artificial intelligence can be particularly helpful. When it comes to the required testing for many programs, it can be time-consuming to program in every variable and every possible outcome from a test. The goal of using artificial intelligence and machine learning is to allow computers to learn on their own to a certain extent so that they can then predict without the programmer monitoring every keystroke. Machine learning essentially allows the computer to do more of the work.

What Does this Mean for the Testing Community?

For those that are wondering how this might change the face of testing, it would allow for the development of a pattern of learning that would help to ensure greater self-learning on the part of computers which would then free up the workforce for other pursuits. By fostering machine learning, testers can then allow computers to make predictions based on correlations between data points and then apply these predictions to new data to improve overall prediction accuracy.

Download the whitepaper below to learn how to make the testing process faster, more accurate, and more succinct, so predictions are as accurate as possible.


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