How to Combine AI and Automation to Enhance Quality Engineering Process?

How to Combine AI and Automation to Enhance Quality Engineering Process?

Overview

In the last ten years, technology has been advancing rapidly, leading to constant changes in the digital world. As a result, organizations are keen on adopting tools and platforms that can help them operate more efficiently and provide better service to their customers. One popular technology that has gained significant popularity is Artificial Intelligence (AI), which is used to optimize processes, gain a competitive edge, engage with customers, develop new business models, and more. When combined with automation, AI creates an even more powerful tool known as AI-assisted automation.

In the field of Quality Engineering, three types of AI have proven to be highly beneficial when integrated with automation: visual AI, self-healing AI, and risk AI. These forms of AI enhance Quality Engineering processes and improve overall efficiency for companies.

What Are Visual, Risk, and Self-Healing AI?

Visual AI is a type of AI that acts like a digital eye, detecting important changes in the user interface and ensuring that the application is displayed correctly. It focuses on factors like readability, usability, and accessibility to ensure a seamless user experience.

When visual AI is combined with automation, it can identify elements on a user interface and automate specific actions. For example, it can locate and click buttons like a login button. This combination simplifies navigation through complex operating systems and languages, even for applications that developers may not have full access to.

Risk AI analyzes change and usage logs to help developers identify at-risk objects in an application and perform impact analysis. It assists in selecting the appropriate tests based on the business and technical risk of the changes. When combined with automation, risk AI prioritizes issue detection, and automation with self-healing AI helps fix test suites with minimal manual effort.

On the other hand, self-healing AI helps adapt test cases and adjust test plans as applications evolve. It ensures that tests and codes remain functional during new iterations by understanding the test context and working around changes without user intervention. It also efficiently handles localized content and interfaces.

By combining self-healing AI and automation, entire application suites can be protected from breaking due to small changes. When a change is made to the user interface, AI can detect any issues, and automation can be programmed to fix them. This minimizes the need for manual effort in resolving problems.

Impact of AI-Assisted Automation on Quality Engineering?

AI-assisted automation has a comprehensive impact on quality engineering. It provides visibility into testing across teams and throughout the software development lifecycle, ensuring quick issue resolution and end-to-end quality. It is particularly useful in areas that involve data analysis, pattern detection, trend identification, and anomaly detection.

Within quality engineering, visual AI testing can validate the readability of text elements during automation. It can detect issues like overlapping elements, off-screen drawing, hidden elements, or problematic color combinations for color blindness. Risk AI can analyze changes made by customers and identify the tests required to address these changes and detect potential failure scenarios. Self-healing AI can automatically fix test scripts that may fail due to changes in data input screens.

AI-Assisted Automation and Humans

AI-assisted automation relieves the entire team from manual efforts, allowing them to focus on more complex and critical tasks. It empowers quality engineers to concentrate on product experience, test coverage, and other strategic aspects of quality assurance. Humans play a crucial role in architecting well and considering processes, implications, and scalability.

With AI-assisted automation, organizations can keep up with the pace of innovation and digital transformation. It optimizes various processes, including quality engineering, by combining visual AI, risk AI, self-healing AI, and automation. This integration enhances issue identification and resolution within applications. As quality engineering is a vital part of the software development lifecycle, AI-assisted automation elevates software engineering and enables faster and more efficient solutions.

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