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 domainspecific 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$\text{8 8 \%}$accuracy in repairing RLFs. Furthermore, a study from software engineers reveals that generated repairs produce visually correct layouts while maintaining aesthetics.

This dataset contains 246 bugs, their fixes and corresponding buggy projects from historical bug fixes dataset (https://github.com/xuanbachle/data-bugfixes) that fulfill the following criteria:\n\n\n\tUnique\n\tSatisfy redundancy assumption at file level\n\tFixed by applying replacement mutation\n\tRequire fixing at expression level\n\tHaving available project and dependency files\n\n\nFor details, please view https://www.scitepress.org/Link.aspx?doi=10.5220/0009411301700180

Patch prioritization means sorting candidate patches based on probability of correctness. It helps to minimize the bug fixing time and maximize the precision of an automated program repairing technique. Approaches in the literature use either syntactic or semantic similarity between faulty code and fixing element to prioritize patches. Unlike others, this paper aims at analyzing the impact of combining syntactic and semantic similarities on patch prioritization. As a pilot study, it uses genealogical and variable similarity to measure semantic similarity, and normalized longest common subsequence to capture syntactic similarity. For evaluating the approach, 22 replacement mutation bugs from IntroClassJava benchmark were used. The approach repairs all the 22 bugs and achieves a precision of 100%.