Computers run today’s world.
Well, this may turn out to be a debatable statement as it is a fact that human beings actually run computers but, here the point is that today, there is hardly anything that doesn’t involve the use of computers.
Computers have been making our lives easier for a long time, and with their exponential growth, a new pathway to achieve greater efficiency has opened with the name of Artificial Intelligence.
Like every other popular field, software testing has also benefited from the use of AI, and one of the most useful AI-based software testing features is Computer Vision. We will take an in-depth look at it and its role in test automation.
Computer vision is the definitional name of computers’ ability to look at digital images, videos, and various other forms of visual inputs, and analyze them to derive meaningful information that can further help in making decisions. While AI in general is the capability of computers to think like humans, computer vision is their ability to see and understand as humans do.
Now, it may seem like computer vision is the same as human vision. Well, their working method is pretty much the same but, the difference is that human beings see things and their lifetime of memories & knowledge helps them process what they see (e.g. what the object is, whether it is moving or still, how far it is, etc.), and on the other hand, computers see things and they only have the already added database in them to analyze the visuals and figure out more details about them.
The primary reason behind using computers is to improve the efficiency of everything that has traditionally been done by human-centric methods. Hence, in the case of computer vision, it must be able to analyze and understand the things that they are seeing at a much higher level than the capacity of human beings. This is a rapidly growing domain in technology, and you can definitely see an unimaginable pace of growth in the near future.
Now, we will take a look at more details about it.
You may already know that the foundation of every AI functionality is huge databases. Similarly, computer vision runs on huge chunks of data that are added to the system, and some of the chunks that it recorded.
The basis of computer vision’s working is that the computer sees different things, analyzes their visual aspects, compares them with the databases, figures out different details about them, and makes its decisions accordingly.
Machine learning does its job by making the computer learn a lot about different objects through huge data sets. So, when the computer looks at anything, it has powerful algorithms helping it with useful information to compare it with, recognize it, and figure out more details about it.
A CNN breaks images down into pixels, gives tags & labels to them, and uses those labels to perform convolutions and make predictions about what it sees. Convolutions are run by the neural network to check the accuracy of its predictions in a series of iterations until the predictions start to come true. It is the computer’s way of recognizing images in a way humans do.
CNN’s working procedure is highly similar to human beings. As human vision does for an image at a distance, it also notices hard edges and simple shapes first. Then it fills in information while running iterations of its predictions.
While CNN is able to understand single images, it is not that capable of recognizing series of frames that are related to one another. For that, Recurrent Neural Network (RNN) is used in video applications.
Well, it has much more to do with test automation than you even imagine. So, let’s dive into it.
Desktop applications are often considered to be one of the most important ones in the professional world. So, you must first know how computer vision shows its magic in testing them.
Popular software products are mostly cross-platform. That means they can be used in different operating systems such as windows, Linux, and macOS. And, it is a common scenario that they will behave differently on different platforms. In those cases, instead of the time-consuming practice of writing different test scripts for them, analyzing their behavior with computer vision turns out to be much more efficient.
That’s why the most renowned advanced AI-based test automation tools effectively use of computer vision to look at the interfaces of desktop applications, analyze the visuals, understand the logic & objective behind them, and help create effective test cases to check their performance and efficiency.
Though desktop and web-based applications are quite popular in the professional world, mobile applications can be considered the most widely used ones. Being an easily accessible device, mobile is tremendously used by all classes of people, and as a result, there is always a high demand for useful applications as well as effective features for mobile devices. So, let’s see how computer vision improves the testing process of mobile applications.
For a better understanding, let’s see what the scene is with using Appium, one of the most popular mobile application testing tools. Well, though a normal Appium framework can easily test regular mobile software products, it becomes incapable of detecting the elements in content-based apps like mobile games. That’s because, in such apps, there are numerous components such as buttons & fields that human eyes can detect while Appium sees only the black screen.
On the other hand, computer vision looks at those components as human eyes do. It recognizes them and guides you to create useful tests for the app. Hence, using computer vision algorithms with Appium or using an efficient test automation tool like Preflight that comes with in-built AI features is the best way to perform mobile test automation.
It is one of the most crucial as well as highly advanced use cases for computer vision. User interfaces are the first thing about apps that users notice and get attracted to. So, UIs play a huge role in getting more users for your product.
Now, as UIs are so important, they must be thoroughly checked for perfection, and you surely have read everything about it in our articles on UI testing. But, the thing that you must know is that even small changes made to a user interface can cause most of its test cases to fail.
To get more ease in the UI development process, features like UI constructors are used. While that is a good way to make the development process easy, it binds you with a great dependency that you must make sure that your app is using the most relevant components’ versions by tracking them.
However, the issue which computer vision solves is that in traditional testing methods, if any change is made to the UI as well as to its components, the existing UI test cases become extremely likely to break. On the other hand, computer vision lets you create test cases by looking at the UI, analyzing it, and understanding the objective behind the components present there. Hence, if any changes are made to the UI, the computer can see it and help you update the test cases so that they do not fail.
Preflight is super proud of its efficient computer vision feature that easily recognizes every change in the UI and helps the tester update the test cases accordingly. You can know more about it along with more advantages of using this amazing tool from the article “10 Amazing Software Testing Benefits From Preflight In 2022”.
Till now, you saw how computer vision improves the test automation experience in conventional devices only. But, there are various more devices that also have different apps as well as their testing needs.
Besides desktops, laptops, mobile phones, and other standard devices, modern tech users highly use devices like gaming consoles, different internet of things devices, additional screens for devices, etc. All such devices have quite different screen formations and different apps that cannot be tested by conventional test cases built for standard devices.
However, the good news is that computer vision is capable of identifying the components of those apps too. They look at the screens, capture screenshots whenever necessary, analyze them, and create tests accordingly. Hence, you can just find ways to use it with maximum efficiency and stay away from any worries about test automation.
From all the discussion above, you may have got internal support that computer vision is completely great for automating your tests but, the reality is totally different.
Although computer vision can provide you with a lot of advantages, similar to other AI functionalities, it still has a lot of research and advancement to be done. Also, it can sometimes be so difficult to effectively implement it into the existing testing environment that you will end up needing much more technical expertise than you would need in traditional testing methods.
Having said that, computer vision is supremely useful for applications that get frequent changes in the UI, and the efficient practice must always be using it through an advanced test automation tool like Preflight.
Preflight carries a great legacy of successfully using various AI functionalities including computer vision to serve its users. Making use of such amazing features has enabled us to make our users capable of creating, running, and managing complex test cases within seconds irrespective of their coding knowledge.
To experience the simplest test automation process, you are most welcome to book a demo with us. And, for any queries, feel free to reach out to us or visit our website. Also, if you are a fan of informative tech articles, do consider checking out our blog page.