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.

Why AI? What are the advantages of AI in Automation Testing?

No unattended errors. The testers can take a back seat and let AI perform the tests with less or no intervention. As soon as a bug is introduced, the AI alerts the tester of the error, why it failed and what could be a potential fix for it and doing quick fixes too.

Improved quality. AI not only makes testing faster but also improves the quality by processing huge amounts of data at a time to identify similar error trends and identifying anomalies.

As the AI testing process is automated, the software developers and testers will get a quick feedback report on the working and the efficiency of the applications. Also, the bugs will quickly be resolved and hence, the products can be launched faster into the market.

Actionable Continuous Feedback is crucial in DevOps.
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.

Effective Automation Testing. With AI in automated testing, can increase the overall depth and scope of tests resulting in the overall improvement of software quality. Automated software testing can look into data sets, locator values, repositories, internal program states, in order to determine if the software is behaving as expected.

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.

AI driven test automation can manage repetitive tasks to meet the continuous delivery demands for increased productivity.

Less expensive. AI reduces the reliability of manual testing methods by reducing a lot of the manpower resources and also the intensive costs.

AI is well suited for Regression testing to compare the result trend with the existing code to identify all the affected areas so that the developers can work on them.

It is not required that the entire test suite be run for every small change that is made. The AI can recommend what tests are affected due to change.

Automatically locates and identifies hundreds of selectors and self corrects/heals them if one selector fails. Testsigma uses a dynamic locator strategy to identify elements that make the tests more robust and reliable with reduced maintenance efforts.

Fully autonomous test creation utilizing AI technologies via natural language processing (a hassle free scriptless test automation approach) and advanced modeling and can point out poor coding. With this information, the DevOps teams may work better in order to produce error-free results.

Test Maintenance. With AI in many areas in automation, testers need not update test cases and continuously keep track of the changes. AI in Test Automation allow tests to be auto corrected to some extent, maintaining all the affected tests automatically in one go!

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.

Conclusion

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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