Filippo Ricca

Page Objects (POs) are a widely adopted design pattern for improving the maintainability and scalability of automated end-to-end web tests. However, creating and maintaining POs is still largely a manual, labor-intensive activity, while automated solutions have seen limited practical adoption. In this context, the potential of Large Language Models (LLMs) for these tasks has remained largely unexplored. This paper presents an empirical study on the feasibility of using LLMs, specifically GPT-4o and DeepSeek Coder, to automatically generate POs for web testing. We evaluate the generated artifacts on an existing benchmark of five web applications for which manually written POs are available (the ground truth), focusing on accuracy (i.e., the proportion of ground truth elements correctly identified) and element recognition rate (i.e., the proportion of ground truth elements correctly identified or marked for modification). Our results show that LLMs can generate syntactically correct and functionally useful POs with accuracy values ranging from 32.6% to 54.0% and element recognition rate exceeding 70% in most cases. Our study contributes the first systematic evaluation of LLMs strengths and open challenges for automated PO generation, and provides directions for further research on integrating LLMs into practical testing workflows.

Web applications play a crucial role in our daily lives, making it essential to employ testing methods that ensure their quality. Typically, Web testing automation frameworks rely on locators to interact with the graphical user interface, acting as connection points to the elements on a Web page. Nevertheless, locators are widely recognized as a major vulnerability in Web testing, as they are highly sensitive to the frequent changes in Web page structures caused by rapid software evolution. The adoption of the Page Object pattern to separate test logic from structural layout - supporting code reuse and maintainability - has generally led to more robust test cases. However, their implementation is a manually intensive task, and even automated support may require manual realignment efforts. Although gamification strategies have recently been integrated into the Web testing process to boost user engagement, using tasks and rewards aligned with testing activities, they have not yet been employed to enhance the robustness of locators and support the implementation of Page Objects. In this paper, we introduce TESTQUEST, a tool designed to improve test robustness by applying gamification to locators and Page Objects, boosting user engagement while guiding them toward the adoption of best practices.