Today, testers need to highly 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. And the scope of automation testing clearly has the right potential to change the direction of QA industry.
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 local environments and eliminate environment-related schedule delays. Still, 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 proves to be an useful aid to the testers to analyze and revamp the automated testing process. It enables testers to automate test cases, providing better efficiency and accuracy.
But AI in Automation?
AI drives automation, performs faster to identify errors and causes, suggests fixes and connect a set of related tests. This not only makes test automation faster but also more precise.AI is capable of automatically accessing data, running tests and identifying errors and 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 self-sufficient that is trained to manage huge amounts of data accurately, and the rate of error introduced is sure to reduce. So, you can not only use our AI-platform to execute code-less testing, but also manage your test data without any hassle.
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.
What is Automation Testing?
Automation testing is the process of automating and checking software or applications under development (or developed) against user requirements. Typically, Automation testing includes validating different elements and modules of the application by executing test cases using automation tools. With Automation testing, you can eliminate the need to manually execute the test cases. Instead, testers can automate the test cases to save time and cost.
The scope of Automation testing is especially useful in running repetitive tasks that take up valuable time of QAs. For the uninitiated, Automation testing undertakes a lot of different testing types where you can simply run the test cases over and over again with several inputs. You can automate some aspects of Functional testing, Regression testing, and API testing easily, which will help speed up your overall QA process.
Why AI?
AI is the technology of today and the future that is positively influencing every industry sector, including Automation testing. As one of the biggest contenders for the future scope of Automation testing, AI helps understand complex data and handle redundant tasks. Moreover, it is useful in predicting potential bugs and analyzing output information. We discuss all of this in detail in the next section.
What are the Advantages of AI in Automation Testing?
AI is bringing a whirlwind of changes in every department, and Automation testing is not immune to it. So, here are some of the advantages of AI in Automation testing that will help your business soar:
No Unattended Errors
The testers can take the 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, the reason for failure, and possibility of a potential fix. It further analyzes the scope of automation testing to suggest quick fixes.
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.
Quick Feedback
As the AI testing process is automatic, the software developers and testers will get a quick feedback report on the working and the efficiency of the applications. Also, there is the quick resolution of the bugs, which reduces the time-to-delivery and helps to launch the products faster into the market.
Actionable Continuous Feedback is Crucial in DevOps
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 assists the DevOps team to ensure that the application works flawlessly.
Effective Automation Testing
With AI test automation, you 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.
Manage Redundant Tasks
AI driven test automation can manage repetitive tasks to meet the continuous delivery demands for increased productivity. 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.
Less Expensive
AI reduces the reliability of manual testing methods by reducing a lot of the manpower resources and also the intensive costs.
Smart Recommendations
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.
Autonomous
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 (scriptless test automation) 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!
Extensibility
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.
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.Try Testsigma now!
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