Authors : Payal Chede; P. A. Tijare

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/uaemvhxj

Scribd : https://tinyurl.com/5n8mw8r8

DOI : https://doi.org/10.38124/ijisrt/26jun1312

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Modern software applications undergo frequent interface changes, making traditional automated user interface (UI) testing increasingly difficult to maintain. Conventional test automation frameworks that rely on static element locators are often vulnerable to layout modifications, attribute changes, and evolving web components, resulting in unstable test execution and increased maintenance effort. This paper presents an intelligent framework for resilient web UI test automation that integrates explainable visual validation and adaptive learning mechanisms to enhance the reliability of automated testing. The proposed approach employs multi-factor similarity analysis to identify alternative UI elements and utilizes visual comparison techniques to validate healing decisions. An adaptive knowledge repository continuously captures successful execution patterns and improves future test runs. Furthermore, the framework provides human-readable explanations that increase transparency and user confidence in automated decisions. Experimental evaluation demonstrates improved robustness, reduced manual intervention, and enhanced test stability when compared with conventional UI automation approaches. The proposed framework offers a scalable and interpretable solution suitable for modern continuous integration and continuous deployment environments.

Keywords : Web UI Testing, Adaptive Learning, Explainable AI, Visual Validation, Test Automation, Software Quality Assurance.

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