Testing is a very important phase in the development process. It ensures that all the bugs are ironed out and that the product, software or hardware, is functioning as expected or as close to the target performance as possible. Even so, some tasks are too laborious to be done manually even though they are easy enough to do. This is where automated testing comes in.
It is a software testing technique to test and compare the actual outcome with the expected outcome. This can be achieved by writing test scripts or using any automation testing tool. Actually, automation testing means using an automation tool to execute test case suite. Automated testing tools are capable of executing tests, reporting outcomes and comparing results with earlier test runs. Tests carried out with these tools can be run repeatedly, at any time of day.
Which Test Cases to Automate? Test cases to be automated can be selected using the following criterion to increase the automation ROI
Machine learning is being successfully applied now in all walks of life, so the question is, how will machine learning and artificial intelligence influence automation testing. Machine Learning in “Test Automation” can help prevent some of the following but not limited cases:
Machine learning applies artificial intelligence to provide systems the ability to automatically learn without human intervention or explicit programming. Systems and testing automation would improve from experience and would automatically access data, run tests with it and learn from the results and improve the testing cycle. Machine Learning is good at categorizing data and can potentially be used for discovering algorithms and detecting patterns. AI can/will take the job of test automation in near future. All we have to do is understand small pieces of automation way and help computers to understand ‘those pieces’.
Machine learning, which teaches systems to learn and apply that knowledge in the future, makes software testers come up with more accurate results than traditional testing ever could. Not to mention that the probability of error is not the only thing that gets reduced. The time needed to perform a software test and find a possible bug is also shortened, while the amount of data that needs to be handled can still increase without any strain on the testing team.