{"ID":2847329,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.00678","arxiv_id":"2511.00678","title":"Repairing Responsive Layout Failures Using Retrieval Augmented Generation","abstract":"Responsive websites frequently experience distorted layouts at specific screen sizes, called Responsive Layout Failures (RLFs). Manually repairing these RLFs involves tedious trial-and-error adjustments of HTML elements and CSS properties. In this study, an automated repair approach, leveraging LLM combined with domain-specific knowledge is proposed. The approach is named ReDeFix, a Retrieval-Augmented Generation (RAG)-based solution that utilizes Stack Overflow (SO) discussions to guide LLM on CSS repairs. By augmenting relevant SO knowledge with RLF-specific contexts, ReDeFix creates a prompt that is sent to the LLM to generate CSS patches. Evaluation demonstrates that our approach achieves an 88\\% accuracy in repairing RLFs. Furthermore, a study from software engineers reveals that generated repairs produce visually correct layouts while maintaining aesthetics.","short_abstract":"Responsive websites frequently experience distorted layouts at specific screen sizes, called Responsive Layout Failures (RLFs). Manually repairing these RLFs involves tedious trial-and-error adjustments of HTML elements and CSS properties. In this study, an automated repair approach, leveraging LLM combined with domain...","url_abs":"https://arxiv.org/abs/2511.00678","url_pdf":"https://arxiv.org/pdf/2511.00678v1","authors":"[\"Tasmia Zerin\",\"Moumita Asad\",\"B. M. Mainul Hossain\",\"Kazi Sakib\"]","published":"2025-11-01T19:41:20Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[\"RAG\",\"Large Language Model\"]","has_code":false}