Enhancing Web Test Script Repair Using Integrated UI Structural and Visual Information

End-to-end UI testing plays an indispensable role in web testing. However, the maintenance of UI test scripts can become a challenge as web applications undergo changes, leading to the potential breakage of these scripts. The manual repair of broken scripts is a time-consuming and labor-intensive process, making it imperative to study automated repair approaches. Existing approaches have relied on either the Document Object Model (DOM) or visual information alone to repair broken scripts, which show limited effectiveness as they only utilize a subset of the available information. Furthermore, merely combining the two approaches is not sufficient to improve effectiveness, as the use of two disparate methods may result in conflicting repair outcomes. In this study, we present a novel approach to web test repair that considers both information in the DOM and UI. To optimize the utilization of this information, our method classifies it as either identity-related or appearance-related, subsequently prioritizing its application in the repair process. In addition, we propose a more advanced lightweight Convolutional Neural Network based approach for better processing visual information. Our approach has been implemented as a tool named Webrl, which is available for practical use and further research. The effectiveness of Webrl was evaluated on a set of broken UI scripts constructed from 38 real-world web sites and was found to outperform the state-of-the-art approaches by a significant margin.