Machine Learning is often tricky for a few as people mistook and sometimes have a vague understanding. Let's attempt to change this affair, here we'll discuss the main principle behind the favored AI algorithm and appearance deeper at the benefits are often delivered to us. So, without further delay, let's talk science.
What is Machine Learning And How does it Work: We perceive the term Artificial Intelligence with very wide latitude, having many stereotypes like 'AI is a Skynet' or 'AI is just a set of If-Else statements'. So what it is exactly and then what's Machine Learning? and How AI is changing Automation Testing?
Artificial Intelligence is an extensive concept holding narrower terms under its shelter, if summed it can be understood as AI addresses the use of computers to mimic the functions of humans, whereas Machine Learning is a technique that realizes AI. If I take it one step forward then, Deep Learning is for realizing ML, with the ability to use deep neural networks.
How does ML works: To make machines work we need one basic thing that is DATA, lots and lots of data. Now Question arises For What? To make computers learn on their own from the model that is being built using data. To clear up confusion let's take a look at one of the examples, say if we need to create a program for recognizing Dogs if approached in old fashion we need to set a plethora of rules. But this program is worthless if there is an image of the dog. You need to set rules again right from the start needless to say time-consuming. ML makes life a lot easier as training is conducted by providing a huge dataset of dog images and teaching it to find its own patterns by connecting dots, testing over and over again to have the best version running.
Application of Machine Learning for Automated Testing: From Self driving cars to Google's language translation app, ML has bought a new generation of automated testing tools. In the current era, the whole range of tasks is driven by computers rather than manual efforts, indicating Automation testing is standing next in the line.
ML-based automation testing can show great results provided we need to know how to exploit it correctly. Here you can apply things correctly and see all the work done is done instead of you. So what's the role of Machine learning here?
In the daily tasks of testers, there can be plenty of cases when results of Functional, Performance, and Load Testing may have some patterns. In such cases, ML can rescue us and make recognition of these patterns easier. Here, ML engineer needs to determine features in data that express patterns, he can collect the right data and feed it to the right algorithms.
Practical applications that we have here are: there are advanced software testing tools like Tricentis, which can be used with minimum inventions.
Another example we can have is: AI testing framework is TestCraft which is codeless Selenium that allows handling procedures faster.
Finally, we can sum up all in all as Machine Learning has a big impact on making the process Quality analysts faster. ML when coupled with Automation can execute cases 24*7, increasing coverage, reducing time, and extensive testing.