Treffer: Screenshot testing as a multi-aspect type of automated dynamic verification for web applications

The subject of this study is multi-aspect screenshot testing as a modern method of automated dynamic verification of web applications, combining functional testing and user interface (UI) validation. Contemporary testing methods face challenges such as high labor intensity, false positives, and low scalability, especially in complex projects. The main objective of the research is to develop and implement a method that improves defect detection accuracy, reduces testing time, and lowers test case development costs. The study explores image comparison algorithms, dynamic element filtering techniques, and automated UI analysis approaches to enhance efficiency and standardization in the web application verification process. Unlike functional and UI testing conducted separately, the proposed method enables simultaneous analysis of multiple aspects of the interface and functionality, minimizing labor costs and increasing testing reliability. The approach employs automated comparison of reference and test screenshots at the pixel, structural element, and content levels using Python, Selenium, PIL, and Pytest-xdist for parallel test execution, effectively addressing the challenges of web application verification. Some researchers in the field of testing agree that the testing process lacks standardization and clear evaluation criteria. The proposed method ensures the achievement of verification objectives even under evolving strategies and approaches to system performance assessment by creating a flexible and precise validation system that integrates various testing types into a unified structure, making it suitable for modern software development challenges. The experimental section demonstrates the advantages of multi-aspect screenshot testing over other methods, including reduced testing time, improved defect detection accuracy, and enhanced analysis of test reports. This approach can be adapted to various testing scenarios and is particularly beneficial for high-load projects requiring regular regression testing.