There are many explanations on the net, Machine learning can be a complex subject for non-experts. People often misunderstood and have a blurry perception of what is AI software testing technologies. Here we will discuss the main principle after one of the most common AI algorithms and look below at what advantages it can bring to Automated testing. So, without further ado, let’s discuss science!
What is Machine Learning and how does it work?
Having loads of stereotypes like ‘AI is a Skynet’ or ‘AI is simply a set of if/else statements’, we still look this term with wide range. So what is AI, and what’s then machine learning? How AI is revolutionizing test automation? Let’s make a leap into the world of cutting-edge technology precisely.
Artificial Intelligence is a wide concept, or more specifically an umbrella holding narrower terms and more practical ways like machine learning and deep learning. The two latest terms are subfields of AI that in particular are the most wide-spread.
- The main purpose of AI is to addresses the application of computers to simulate the cognitive functions of humans.
- Machine Learning is a method for realizing AI.
- Deep learning is a method for realizing ML. It is the knowledge of underlying features in data applying deep neural networks.
How does machine learning work? To make machines learn we require data. We need loads of data. For what? We need to accumulate it, build a model, and then allow computers to learn on their own. What does it mean in usage is a much greater question. To clear up all the confusion, let’s take a peek at one of the famous examples.
Say we have a job to create a program for identifying cats. If approaching this job in an old-fashioned way, we require to set plenty of explicit rules like the color and form of eyes and so on. But what would the plan do when you give it a picture of a dog? You require to set all the rules again immediately from the start. Needless to say, such a method is time-consuming.
With machine learning, we do everything differently: we let computers do this job without explicit programming methods. For this, we just require to give a machine a large dataset of cat photos and explain it to find its own patterns. It joins the dots, pretty much randomly at beginning, but you test it over and over, retaining the best versions.
So, what’s then deep learning? This training can be carried in several stages – every stage is a neural network layer. The more layers, the more complex the model we can create. The structure of a neural network can consist of multiple layers, information processing is divided into various stages. This is where the “deep” originated from, by the way.
Applications of Machine Learning for Automated Testing
From Google’s language translation app to self-driving cars, Machine learning has got us a new age of smart automated software testing devices. Right now a complete spectrum of jobs are being driven by computers rather than manual labor, automated testing is following in the line.
ML-based automation testing can give great results, but just on one condition – you know how to exploit it accurately. It’s not a magical way you can just apply and view all the work done rather than you. So, when we discuss Automation testing, what then Machine learning can give for it?
In the daily work of testers, there are lots of cases when results of load testing, performance testing, or functional testing have some important patterns. In such situations, ML can come to the saving and make recognition of these patterns simpler.
For this, the ML engineer has to decide which features in the data might be utilized to express important patterns. Then he accumulates and disputes the data, finds the accurate data and the correct algorithm to feed.
What are the practical applications for this?
Here are some of the most common cases:
- Saving on Manual Labor of writing test cases
- Test cases are fragile, so when something goes wrong, a framework is most prone to either drop the testing at that time or to skip some steps, which may occur in a wrong/failed result.
- Tests are not confirmed until and unless that test is driven. So, if a script is written to verify for an “OK” button, then we wouldn’t comprehend its existence until we operate the test.
Despite the hard work of ML engineers, you can also obtain ready-made solutions powered by machine learning. These are advanced automated software testing devices like Tricentis and Telerik. You can utilize them to carry out the method of testing with minimum interventions.
Another strong example of an advanced AI testing framework for automation is TestCraft. It is a codeless Selenium that enables handling testing procedures quicker, as it creates a dynamic test model that can be simply updated to reflect modifications to your app.
TestUnity uses Test automation tools linked with machine learning to perform test cases. This improves test coverage and decreases the time needed for extensive testing of the product. Visit TestUnity blog to learn more about the latest trends in the world of technology and Quality Assurance / Quality Control processes.