By WalkingTree   September 04, 2020

How AI improves Microservices Testing Automation

Organizations that adopt AI in the testing of microservices-based applications have better accuracy, faster results, and greater operational efficiency. AI and machine-learning technologies have become more popular over the last few years, and today their application in automated testing can help in more ways. In fact, AI has redefined the way microservices-based applications are tested, especially when it comes to canary testing. 

The use of AI in software testing helps both developers and testers. Automated testing can increase both the depth and scope of tests, resulting in more thorough overall test coverage. 

AI-driven test automation

Software testers can take advantage of AI for test creation and execution, data analysis by using natural-language processing modeling techniques. AI-based software testing can help by increasing efficiency, facilitating faster releases, improving test accuracy and coverage, and allowing for easier test maintenance. For efficient test maintenance, developers need to know what is happening to the data at the time of test creation. AI can help with efficient data modeling and root-cause analysis.

Use AI for canary testing

Canary testing helps reduce risks by rolling out the changes to a small group of users before presenting it to a larger audience and it is particularly useful in the testing of microservices-based applications. The changes to microservices happen independently of one another, so those microservices need to be verified independently as well. AI can automate canary testing of microservices-based applications. 

Read on to know more about canary testing and how AI can help automate microservices testing automation.

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