Making AI-Driven Test Automation Easy

1. Making AI-Driven Test Automation Easy we are on the verge of making AI-powered self-driving cars a reality, why not AI in Test Automation, where 80% of the testing tasks are repetitive and can be automated? As such has to evolve to meet today’s demanding requirements faster and better. In a world where AI is being used everywhere to make life simpler and faster, AI-driven Automation testing tools without coding is no exception! In the era of AI being used to make things simpler and faster, AI-driven Test Automation is no exception. Testing tasks must be automated at least 80% of the time, while the remaining 20% can be Manual. As development teams shift toward Continuous Testing, the Test Automation approach must become smarter to be more effective in the delivery pipeline.

2. Application of AI in Test Automation Here are a few simple ways to use AI in test automation. 1.test runs and executions Creating a test run with all affected and relevant test cases to mitigate the company risks for each change in the application is a tedious and time-consuming process that requires significant amounts of time.

3. New functionality, it is also important to test existing features before introducing and testing them, are outdated tests, so creating a test run that covers all company risks is time-consuming. Considering the cost overheads, it isn’t practical to test everything at once, creating a test execution is therefore a critical step to cover all the problem areas and to speed up a test run without leaving anything open takes a lot of time and collaboration, the demand for software applications to be released faster rises, one may need to create effective test runs faster. it is unlikely that it would be 100% effective with humans. Not everyone will know the application end to end to identify the affected test cases correctly, it would be difficult to go over every test case manually in short delivery cycles even if it were possible, it is only admissible if some test cases are left out. Whenever machines come to rescue, they do it, planned and what is to be developed in a specific

4. format, machines can quickly create plans, Consequently, there are no potential human errors. Testing runs and executions are made faster and more robust by AI in Test Automation, making test runs and executions faster and more robust by continuously monitoring application quality. 2. Test Case Maintenance Maintenance is a significant and ongoing task in Test Automation. there is a need to retrace the affected automated test cases to update the tests as a result of a change in the application. It may not always be just one or two simple tests that need to be updated.

5. a change may require several direct and indirect automated tests, not just one. Every change needs to be identified and tested to see if there is anything wrong with the system. as the application changes, maintenance efforts increase even more as the application’s functionality improves. Scriptless automation tools maintenance tasks can only be reduced to a certain extent in GUI Automated Testing using a central repository. For today’s development paradigms, this isn’t appropriate. But also reduce the overall maintenance effort required, Possibly more importantly, to make automated testing more reliable and stable. Instead of using static locators to locate an element in the UI, a dynamic locator approach can be used to locate elements that make the tests more robust and reliable with minimal maintenance effort,

6. and can be autocorrected to a certain degree, keeping all the affected tests automatically in one go! With AI, we will be able to identify all the tests that are directly or indirectly related to a change in the application. 3. Learn From Test Failures When a failed test failed, testers took some time to figure out the root cause of the problem before being able to fix it. and, if the issue had arisen as a result of a system-level change or a change in the application behavior. Instead of fixing the issue, the team will be looking for other tests that may have been affected.

7. Investing time and manual labor is wasted, they would still need to debug all failed tests manually one at a time before filtering similar failures. With all the information available, we can imagine using machines to detect similar failures in other tests without running all the tests again and not having to debug each failed test . We can run other failed tests or the tests altogether, giving you a smaller number of failed tests to debug if there are any. A failed test will help us identify and fix similar tests that may have been affected automatically with AI in Test Automation. We can record how a failed test performed over a period of time, causing the test to fail, and use the same results to identify other tests that may have been affected as well.

8. AI also comes up with test runs to run for forthcoming releases quickly, allowing more stable releases to be performed faster due to the test result history. 4. Test Development Test development is a discipline that requires a lot of initial effort and effort, as well as time and expertise in a Test automation project. Among the AI-based potential solutions to reduce these efforts are: 1. from requirement documents by using Natural Language Processing techniques. 2. We can easily navigate through all the pages and run automated tests to address all possible scenarios with a UI based framework. 3. Train Test Bots to perform certain tasks with input data. This may look similar to BDD, but it will be faster and easier if AI is used in Test Automation. 4. Visual recognition for GUI applications such as Google’s image recognition that use visual images to look for changes in the document.

9. Some of the potential areas for AI in Test Automation are listed below. Eventually, AI would be able to design, execute, and maintain automated tests with human supervision rather than with human intervention. FIREFLINK has taken a step toward implementing a #Smartautomated testing tools in software testing platforms that use AI in Test Automation in the areas discussed above. With the advent of development techniques such as Agile and DevOps, we understand that the same old legacy automation testing tools and techniques are no longer appropriate today. There is a lot of potential for AI in Test Automation with a need for better automation testing techniques.

10. maintenance tasks can only be reduced to a certain extent in GUI Automated Testing using a central repository. For today’s development paradigms, this isn’t appropriate. We need more intelligent approaches not just to change the directly impacted tests , but also reduce the overall maintenance effort required. Possibly more importantly, to make automated testing more reliable and stable. We could see much more using AI to solve some of the biggest challenges in Test Automation in the coming days to make automated testing tools faster, easier, and cheaper for the next generation, as FIREFLINK did from day one! #FireFlink Click on to know — http://fireflink.com/