{"id":15699,"date":"2025-11-26T16:27:02","date_gmt":"2025-11-26T15:27:02","guid":{"rendered":"https:\/\/www.uni.lu\/research-en\/?post_type=events&p=15699"},"modified":"2026-02-12T10:33:45","modified_gmt":"2026-02-12T09:33:45","slug":"doctoral-defence-hilal-taha","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/research-en\/events\/doctoral-defence-hilal-taha\/","title":{"rendered":"Doctoral Defence: Hilal TAHA"},"content":{"rendered":"\n
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The Doctoral School in Science and Engineering<\/a> is happy to invite you to Hilal TAHA\u2019s defence entitled<\/p>\n\n\n\n

\nMaintenance of UI Tests in Evolving Web Applications<\/h2>\n\n\n\n

Supervisor<\/strong>: Assoc. Prof Michail PAPADAKIS<\/a><\/p>\n\n\n\n

Web applications have become the primary interface for delivering online services, and their reliability is critical for user experience and business continuity. Automated End-to-End (E2E) testing is essential for validating complete workflows through the Graphical User Interface (GUI). However, the dynamic nature of modern web applications, which is driven by rapid development cycles, frequent User Interface (UI) changes, and evolving Document Object Model (DOM) structures, makes maintaining these tests a persistent challenge. Locator breakages, which stem from element identifiers becoming obsolete, account for the majority of test failures and significantly increase maintenance costs while eroding confidence in automated testing.<\/p>\n\n\n\n

This dissertation addresses these challenges through three complementary contributions. First, we introduce a large-scale dataset of DOM evolution across 181 actively maintained websites, including up to 1,000 historical versions per site with ground truth annotations for DOM position stability and locator breakage detection. This dataset enables reproducible research on robust locator generation, test repair, and web evolution analysis\u2014areas previously limited by the scarcity of representative, real-world data.<\/p>\n\n\n\n

Second, we propose EAGL (Evolution-Aware Generation of robust Locators), a data-driven approach that predicts DOM position stability using six lightweight structural features and constructs XML Path Language (XPath) locators anchored at stable positions. Unlike heuristic-based methods, EAGL leverages these stability predictions to identify structurally persistent anchors, improving locator robustness without compromising precision. Comprehensive experiments on real-world websites demonstrate that EAGL outperforms both state-of-the-practice (Selenium) and state-of-the-art (Robula+) approaches in precision and robustness while maintaining practical efficiency. Third, we present an industrial replication study of the Correct Locator Recommender (COLOR) repair tool deployed in a privacy-sensitive Continuous Integration (CI) environment. This study analyses failure patterns across thousands of test executions, evaluates COLOR\u2019s effectiveness in repairing broken locators, and identifies practical challenges in integrating automated repair into enterprise work-flows. Our findings demonstrate that while automated repair substantially reduces maintenance overhead, its effectiveness depends critically on attribute richness, locator design practices, and organizational constraints.<\/p>\n\n\n\n

Collectively, these contributions advance understanding of test fragility and provide scalable, evolution-aware solutions for improving web application test maintainability. This work opens pathways for integrating stability prediction into self-healing frameworks, extending datasets to dynamic content, exploring semantic anchors for enhanced robustness, and bridging academic research with industrial practice to enable more resilient, cost-effective, and adaptive web testing strategies.<\/p>\n\n\n\n