Implementation of AI in Software Testing

Artificial intelligence is accelerating software development cycles with its dynamic problem-solving abilities. Software developers are increasingly turning to AI development services for automating and improving one of the most crucial phase, software testing. AI in software testing is an impressive and efficient way toward reducing the burden of tedious tasks for developers and testers. 

What is Artificial Intelligence?

Before we move on closely at how AI could take software testing to another level let’s get to know about what actually AI means?

An interesting definition is that AI is stated as "A study that can provide computers the ability to learn without being programmed."

One of the main points in AI is that there is no need to program algorithms explicitly. Although algorithms are used but certainly not designed for the explicit solution. Machines use data for learning purposes. Hence, machine learning solutions typically require minimal programming and more data to train the model and achieve efficiency. 

Applying AI to Software Testing

So as to meet the challenges, a very simple imperative is followed: Test smarter, not harder

Let’s take an example by considering how image recognition is used to take the UI testing to another level so that UI controls such as responsiveness, etc. can be automatically recognized in all shapes and forms.

UI controls can be recognized from the human perspective, beyond matching the plain template. UI’s pixel structures are interpreted by recognizing the patterns of image and identifying information such as text.

Using AI, the software testing tool can even learn how to identify the controls while scanning and executing the execution process, independent of control colour, text alignment, size, etc. We apply a learning approach, images are generalized by the addition of the new image patterns, and existing images are adapted while scanning and test execution process.

Each control is aware of its context on a graphical interface via “anchor identification”. Properties of controls are extracted from the pattern of an image, and in turn used to constrain automation of certain controls like a scroll bar, while executing test cases. By this, we can eliminate the need to recognize from their technical implementation eg ID, etc.

The result of this learning is that it enables the stable and repeatable test execution even on the responsive UI.

Deploying AI in Software Testing Wih Oodles AI

The need for innovative and robust technology-led solutions calls for businesses to upgrade their techniques for software development. We, at Oodles, enable businesses to integrate AI-powered automation into software testing, and application development with machine learning models. Our experiential knowledge in Pythion and machine learning has benefited global companies to introduce automation into core business processes efficiently.

Connect with our AI development team to explore more about our artificial intelligence and machine learning capabilities.