Testers today need to be equipped with more advanced tools to ensure the consistency of the software delivered at the speed of Agile/Continuous Delivery. Automation testing is unquestionably the most effective way to test in a continuous delivery cycle.
To meet ever-changing business needs will require an online presence and fast-changing application requirements for every company. The changing application requirements in the growing number of devices in a short space by using Automation Testing with an acceptable test coverage is not unrealizable but indecisive.
Scriptless automation testing tools has been made possible by the cloud and SaaS to scale testing from only local environments and to eliminate environment-related schedule delays, but as testing progresses toward greater automation, the next level of automation will be for Artificial Intelligence (AI) and Machine Learning.
With little human input, the AI is expected to assist testers in analyzing and improving the automated testing process (not just test development) with little human input. AI helps the tests to be carried out with greater precision and precision.
AI is intelligent automation.
AI drives automation, and when trained to detect errors and its causes, it performs faster, suggests solutions, and makes a connection of a series of related tests. This not only makes test automation easier, but also more precise.
AI should be able to access data, run tests, and be able to spot an error and also identify other relevant tests that have been affected by the crash. Using this approach, the test’s quality is increased.
FIREFLINK’s AI goes one step further and suggests solutions for a potential problem. AI is able to process large amounts of data efficiently, and the rate of error introduced is likely to decrease as a result.
can make decisions about which locators to use to locate an element if one fails at runtime. There is no need to worry about any improvement in the program because the AI will automatically correct “heal”itself.
Why AI? What are the benefits of AI in Automation Testing?
There are no unattended errors. can take a back seat and allow AI to perform the tests with less or no intervention. The AI informs the programmer of the error, why it failed, and what could be a potential fix for it as soon as it is discovered, as well as doing quick fixes.
Enhanced quality has been achieved. Improves the accuracy by processing large amounts of data at a time to find similar error patterns and identifying anomalies.
As the AI testing process is automated, the programmers and testers are the software developers and testers will receive a quick feedback report on the working and the performance of the applications as the AI testing process is automated. Moreover, the bugs will be quickly fixed, allowing the products to be introduced faster to the market.
DevOps relies on actionable Continuous Feedback.
Read about the benefits of continuous improvement based on actionable feedback. Machine Learning/AI can spot defects early on and make appropriate recommendations in the form of readable and actionable error messages for difficult tests, which can be provided to the DevOps team in order to ensure that the application runs flawlessly.
Scriptless automation testing tools. With AI in automated testing, the overall depth and scope of tests can be increased, leading to the overall improvement of software quality. Automated software testing can look at data sets, locator values, repositories, and internal program states to see if the application is working as expected.
AI can help you choose the right set of tests to be run for the application changes in order to ensure a consistent test coverage with optimized testing efforts that are not possible with just automated testing tools.
AI driven test automation that can be automated to automate repetitive tasks to meet the increasing demand for increased productivity.
Less expensive. AI improves the accuracy of manual testing methods by reducing a lot of manpower and also the high cost of manual testing methods.
AI is well suited for Regression testing to compare the result trend with existing code to find all the affected areas so that the programmer can work on them.
For every small change that is made, it is not necessary that the entire test suite be run. AI will tell which tests are affected by the changes.
Heals hundreds of selectors automatically locates and identifies hundreds of selectors and automatically corrects/heals them if one selector fails. FIREFLINK uses a dynamic locator approach to find elements that make the tests more robust and reliable with minimal maintenance efforts.
Fully autonomous test creation is done using AI tools such as natural language processing (a hassle-free scriptless test automation approach) and advanced modeling, which can spot defects in the code, with this information, the DevOps teams will be able to perform better in order to achieve error-free results.
Ensure that the test is running properly, testers need not update test cases and keep track of the changes with AI in many areas of automation. AI in automated testing tools can be used to autocorrect tests to a certain extent, keeping all the tested tests updated automatically in one go!
To know more click on — https://fireflink.com/
