Hilal Taha

Modern web applications evolve continuously, often causing test breakages when test scripts fail to adapt to changes in the Document Object Model (DOM). Such breakages frequently arise from web locators, which identify the DOM elements that tests interact with. Since locators rely on specific elements and attributes to navigate the DOM, they are inherently vulnerable to changes. To address this, we propose EAGL, a technique for generating robust locators by leveraging the stability of DOM positions—resolving to either element or attribute nodes—predicted from historical DOM changes. EAGL synthesises locators by dynamically selecting stable and unique positions adequate to compose a locator for an element. We constructed a dataset of DOM position changes across 181 real-world websites and trained a stability prediction model that identifies persistent positions. Experimental results show that EAGL achieves strong stability prediction performance, with high classification metrics (precision 0.87, recall 0.85, F1 0.80, ROC-AUC 0.71) and robust ranking capability (MAP 0.83), reflecting its ability to prioritise stable DOM positions. Leveraging these models, EAGL generates robust and precise locators, outperforming Selenium and ROBULA+ while maintaining high efficiency (average locator generation time under 0.25 s). Cross-project evaluations confirm that EAGL generalises well across different websites, demonstrating its practical usefulness.

Web applications evolve rapidly, and UI changes cause automated end-to-end tests to fail. This paper reports a conceptual, independent replication of the COLOR locator repair approach. We reimplemented COLOR from its published description and deployed our implementation in an industrial Continuous Integration (CI) system, where tests are executed automatically on every codebase change. We analysed 4,471 test executions across 133 CI days spanning over seven months, categorised recurring failure modes, and found locator breakage and element visibility issues to be the dominant causes of instability. We analysed failure patterns introduced with evolution, collected element descriptors at runtime and deployed replication of the locator repair technique (COLOR) when locators fail. The repair mechanism suggested fixes for all captured broken locators, with the top recommendation correct in most cases, while exposing limitations caused by initially missing element data, dynamically generated locators, and sparse or volatile HTML attributes. Our results quantify the impact of attribute composition on repairability and highlight practical integration challenges for automated repair in daily CI workflows.