Scope and impact of AI in Automation Testing
Today, testers need to be equipped with more advanced techniques to ensure the quality of the software released at the speed of Agile/Continuous Delivery. Automation Testing is undoubtedly the best approach to testing in a continuous delivery cycle.
Every business will have an online presence and fast-changing application requirements to manage dynamic business requirements. Meeting the changing application needs in the growing number of devices in a short time using Automation Testing with an acceptable test coverage is not unrealizable but indecisive.
The cloud and SaaS have made it easy to scale testing from just local environments and to eliminate environment-related schedule delays but as testing moves towards greater automation, the next level of automation would be for Artificial Intelligence (AI) and Machine Learning.
With little human input, the AI is expected to help testers analyze and revamp the automated testing process(not just test development). AI enables the tests to be automated with better efficiency and accuracy.
Essentially put, AI is intelligent automation.
AI drives automation, performs faster when trained to identify errors, its causes and suggest fixes and make a connection of a set of related tests. This not only makes test automation faster but also more precise.
AI should be capable of automatically accessing data, running tests and being able to identify an error and also identify other relevant affected tests. This approach improves the quality of the tests.
Testsigma’s AI goes one step further and suggests fixes for a possible error. AI is trained to manage huge amounts of data accurately and, the rate of error introduced is sure to reduce.
It can make decisions automatically at runtime as to what locators to identify an element if one fails. And there is less need to worry about any change in the application since the AI can automatically correct “heal”itself.
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Why AI? What are the advantages of AI in Automation Testing?
Read about the importance of actionable feedback for continuous delivery. Machine Learning/AI can identify errors very early and suggest necessary recommendations in the form of readable and actionable error messages problematic tests. The findings and suggestions can be provided to the DevOps team to ensure that the application works flawlessly.
AI can help you right set of tests to be run for the application changes to provide a good test coverage with optimized testing efforts that is not possible with just Automation Testing.
The new era of Smart Test Automation has a lot of scope to become much better with AI.
AI extends the scope of automation testing, making it possible for non-technical team members too to define and scale tests. In short, AI boosts test automation by streamlining creation, execution, maintenance and time-to-market with actionable feedback in real-time.
What are some areas where AI can be applied?
Check out this article that discusses the areas of Automation Testing where AI can be applied.
This method of writing tests first also with the shift left approach has become popularized as a good practice for automation testers. However, it is sometimes not exercised at all. (Why?) With artificial intelligence’s solutions, this would be easier. Businesses implementing AI at the enterprise level are already experiencing greater operational efficiency and better productivity results.
Automation gives organizations an opportunity to replace mundane, repetitive processes. Automation combined with the gathering input, analyzing data, efficiency-finding, and even making decisions of AI will make a great pact for the future and it is now with the new era of smart automation!
And, with the timely involvement of testers to validate their actions, quality is assured.
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