AI for Automation Testing

AI for Automation Testing

The trend is software testing has seen a steady shift from manual testing methods to automated defect tracking tools. With the competitive nature of the industry, it’s to no one’s surprise how delivering applications and software at the fastest rate has become the benchmark to compete for. And with that, comes the requirement to develop quickly and test software intelligently every single time.

According to a study done by the PwC, it is reported that AI alone could impact the global economy by as much as $15.7 trillion by 2030. This makes the role of AI and automation very clear when it comes to disrupting the way businesses conduct their operations.

At present, technologies and methodologies like DevOps, Agile and continuous testing help keep the software development process at a steady pace, but AI could revolutionise the way these operations are done entirely. Artificial Intelligence in testing especially helps to make the software development lifecycles quicker by leveraging the use of proper automated defect tracking tools.

The Benefits of integrating AI in Testing

Testing of UI Interfaces

Image recognition, a serious component of machine learning processes, takes UI testing to a subsequent level. The dynamic UI controls are often documented regardless of their shape and size. AI can analyze the interface to the pixel-level and identify the complex image patterns.

Flexibility in Automated Testing

Simple changes within the application often end in test failures because the testing scenarios often specialise in one segment leading to extensive inflexibility. The AI/ML testing has the potential to perceive and study the associations amongst several segments, providing the flexibility to adapt to real-time changes. Since the test scripts can automatically suit the changes, the testing becomes more sustainable and reliable.

Visual validation automation testing

AI makes use of machine learning and its Pattern recognition capability to look for the visual bugs within the software so as to make sure the visual validation of the software. Visual testing using AI may be a QA method utilised to verify that the interface of the software looks accurate when viewed by the user. It ensures that the various visual components don’t accidentally overlap with each other. This is often extremely useful as there’s a really high possibility of missing the tiny visual errors by a person’s eye which AI recognizes at a significantly faster pace accurately.

Writing Test Cases

AI alongside ML (Machine Learning) is often leveraged to write test cases for the software by crawling through it & collecting the specified data. The dataset is often utilised to train the ML models about the software, the perfect expected pattern, in order that with every new run, it can identify just in case any deviation is detected.

To Conclude

While most businesses may still opt for other means of integration besides AI, the benefits of the latter and most importantly, its place in the future is unparalleled. A vast number of businesses are already shifting towards artificial intelligence for testing and utilising defect tracking tools with automation settings built-in. Kualitee is one such business that emphasises implementing the latest technologies to streamline the software testing process. Get in touch today to see if any of our services benefit your testing process.

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Hannah Montana