March 26, 2021
The software testing has predominantly progressed from manual testing towards incorporation of automation testing. The competitive market and growing technical intricacy have fortified the Software Development Life Cycle(SDLC) to deliver the products to market at breakneck speeds. The need to develop swiftly and test smartly is rising each day.
While the present-day practices of Continuous Testing, Agile and DevOps are keeping the software development process at pace, leveraging the Artificial Intelligence is essential for unlocking the true potential of the software testing.
As per a study by PwC, AI could contribute up to $15.7 trillion to the global economy by 2030.
Undeniably, AI and Automation are disrupting the way organizations execute their operations, enabling them to reinvent their strategies.
AI in testing helps in making the SDLC rapid by supplementing the testers with the right technology and Automation Testing Tools like TestingWhiz.
Testing of UI Interfaces
Image recognition, a major component of machine learning processes takes UI testing to the next level. The dynamic UI controls can be documented irrespective of their shape and size. AI can analyze the user interface to the pixel-level and identify the complex image patterns.
Flexibility in Automated Testing
Simple changes in the application often result in test failures because the testing scenarios often focus on single segment resulting in extensive inflexibility. The AI/ML testing have the capability to perceive and learn about the associations amongst several segments, providing the flexibility to adapt to the real time changes. Since the test scripts can automatically adjust to the changes, the testing becomes more sustainable and consistent.
Visual validation automation testing
AI makes use of machine learning and its Pattern recognition capability to search for the visual bugs in the software in order to ensure the visual validation of the application. Visual testing using AI is a QA method used to verify that the user interface of the application looks accurate when viewed by the user. It ensures that the diverse visual components do not accidentally overlap with one another. This is extremely useful as there is a very high possibility of missing the small visual errors by a human eye which AI recognizes at a significantly faster pace accurately.
Writing Test Cases
AI along with ML (Machine Learning) can be used to write test cases for the application by crawling through it & collecting the required data. The dataset can be used to train the ML models about the application, the ideal expected pattern, so that with every new run, it can identify in case any deviation is detected.
The obvious question that we need to encounter at this point is why we should choose the Integrated Python Testing Platform for AI over others.
A number of companies are already operating in the field of AI-powered testing using the Integrated Python Platform in Automation Testing tools like TestingWhiz.
Are you ready to adopt Artificial Intelligence with Automation Testing to reap the greater benefits? Connect with us to know more.