The goals we are trying to achieve here by using Machine Learning for automation testing are to dynamically write new test cases based on user interactions by data-mining their logs and their behavior on the application/service for which tests are to be written and live validation so that in case an object is modified or removed or some other change occurs, like "modification in spelling," such is done by most of the IDE's in the form of Intelli-sense like Visual Studio or Eclipse.Machine Learning in "Test Automation" can help prevent some of the following but not limited to cases: Saving on Manual Labor of writing test cases Test cases are brittle, so when something goes wrong, a framework is most likely to either drop the testing at that point or to skip some steps, which may result in a wrong/failed result. Tests are not validated until and unless that test is run. So, if a script is written to check for an "OK" button, then we wouldn't know about its existence until we run the test.


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machine learning,artificial intelligence,test automation,automation testing,software test automation