Computer Vision for UI Testing: Leveraging Image Recognition and AI to Validate Elements and Layouts
Abstract
Himanshu Pathak and Rajat kapoor
User Interface (UI) testing is a critical aspect of software quality assurance, ensuring that applications deliver a consistent and user-friendly experience. Traditional testing methods often rely on manual or code-based validation, which can be time- consuming and prone to human error. This study explores the application of computer vision and image recognition techniques to automate UI testing, improving accuracy and efficiency. The methodology involves leveraging machine learning models and pattern recognition algorithms to detect and validate UI elements and layouts. Through experimental analysis, the proposed approach demonstrates enhanced performance in identifying UI inconsistencies across multiple devices and screen resolutions. Testing shows that this method does a better job of finding UI problems across different devices and screen sizes. The results indicate that computer vision-driven UI testing significantly reduces false positives, accelerates test execution, and improves defect detection rates. The discussion highlights the advantages and limitations of this approach, emphasizing the potential for AI-driven solutions to address challenges related to dynamic content and layout variations. The findings suggest that integrating computer vision into UI testing can streamline validation processes, reduce maintenance costs, and enhance software reliability. Future research should focus on optimizing image recognition algorithms and improving adaptability to real-time UI changes.