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With over 60 percent of global Internet traffic originating from mobile devices, Responsive Web Design (RWD) has become essential for ensuring seamless user experiences across diverse screen sizes and resolutions. The Web Content Accessibility Guidelines require that both information and functionality remain accessible when reflow occurs. However, existing checkers or research tools have limitations: they either employ static analysis that fails to capture how content is actually displayed to users or focus on only one accessibility aspect, such as keyboard operation. Consequently, no prior study has comprehensively addressed both information and functionality loss during reflow for Graphical User Interfaces (GUIs). This paper introduces ReFLAIR (Reflow Fault Localization using AI-based Responsive analysis), a multi-modal generative AI–driven approach for dynamically detecting reflow issues that cause loss of information or functionality in the GUI. ReFLAIR systematically extracts informative and actionable widgets, compares their presence and behavior across original and reflowed layouts, and employs both scrolling and large language model–guided expansion to uncover hidden interface widgets. We evaluate ReFLAIR on a dataset of 24 diverse webpages drawn from popular sites, prior benchmarks, and newly released webpages. Results show that ReFLAIR outperforms five state-of-the-art techniques, achieving precision improvements of at least 20.49% and recall improvements of at least 55.40%, while maintaining reasonable computational and runtime cost. An ablation study confirms that dynamic exploration (i.e., scrolling and expansion) is essential for high accuracy. We evaluated scalability and generalizability by extending the dataset to 36 webpages, covering 28 domains and higher complexity, and experimenting with alternative models and viewports. The results reinforce ReFLAIR’s consistency across diverse subjects and configurations. In summary, our approach contributes to accessibility testing by providing an effective, scalable, and cost-efficient solution for identifying reflow issues in RWD. 

Web accessibility is essential for inclusive digital experiences, yet the accessibility of LLM-generated code remains underexplored. This paper presents an empirical study comparing the accessibility of web code generated by GPT-4o, Qwen2.5-Coder-32B-Instruct-AWQ, and Gemini-3-Flash against human-written code. Results show that LLMs often produce more accessible code, especially for basic features like color contrast and alternative text, but struggle with complex issues such as ARIA attributes. We also assess advanced prompting strategies (Zero-Shot, Few-Shot, Self-Criticism), finding they offer some gains but are limited. To address these gaps, we introduceFeedA11y, a feedback-driven ReAct-based approach that demonstrates the potential of incorporating accessibility evaluation results into the code generation process. Our work highlights the promise of LLMs for accessible code generation and emphasizes the need for feedback-based techniques to address persistent challenges. We provide the source code and datasets that were used in our experiments in the companion website [15]. 

Websites are integral to people’s daily lives, with billions in use today. However, due to limited awareness of accessibility and its guidelines, developers often release web apps that are inaccessible to people with disabilities, who make up around 16% of the global population. To ensure a baseline of accessibility, software engineers rely on automated checkers that assess a webpage’s compliance based on predefined rules. Unfortunately, these tools typically cover only a small subset of accessibility guidelines and often overlook violations that require a semantic understanding of the webpage. The advent of generative AI, known for its ability to comprehend textual and visual content, has created new possibilities for detecting accessibility violations. We began by studying the most widely used guideline, WCAG, to determine the testable success criteria that generative AI could address. This led to the development of an automated tool called GenA11y, which extracts elements from a page related to each success criterion and inputs them into an LLM prompted to detect accessibility issues on the web. Evaluations of GenA11y showed its effectiveness, with a precision of 94.5% and a recall of 87.61%. Additionally, when tested on real websites, GenA11y identified an average of eight more types of accessibility violations than the combination of existing tools. 

The pervasiveness of mobile apps in everyday life necessitates robust testing strategies to ensure quality and efficiency, especially through end-to-end usage-based tests for mobile apps' user interfaces (UIs). However, manually creating and maintaining such tests can be costly for developers. Since many apps share similar functionalities beneath diverse UIs, previous works have shown the possibility of transferring UI tests across different apps within the same domain, thereby eliminating the need for writing the tests manually. However, these methods have struggled to accommodate real-world variations, often facing limitations in scenarios where source and target apps are not very similar or fail to accurately transfer test oracles. This paper introduces an innovative technique, LLMigrate, which leverages Large Language Models (LLMs) to efficiently transfer usage-based UI tests across mobile apps. Our experimental evaluation shows LLMigrate can achieve a 97.5% success rate in automated test transfer, reducing the manual effort required to write tests from scratch by 91.1%. This represents an improvement of 9.1% in success rate and 38.2% in effort reduction compared to the best-performing prior technique, setting a new benchmark for automated test transfer. 

Despite the availability of numerous automatic accessibility testing solutions, web accessibility issues persist on many websites. Moreover, there is a lack of systematic evaluations of the efficacy of current accessibility testing tools. To address this gap, we present the first mutation analysis framework, called Ma11y, designed to assess web accessibility testing tools. Ma11y includes 25 mutation operators that intentionally violate various accessibility principles and an automated oracle to determine whether a mutant is detected by a testing tool. Evaluation on real-world websites demonstrates the practical applicability of the mutation operators and the framework’s capacity to assess tool performance. Our results demonstrate that the current tools cannot identify nearly 50% of the accessibility bugs injected by our framework, thus underscoring the need for the development of more effective accessibility testing tools. Finally, the framework’s accuracy and performance attest to its potential for seamless and automated application in practical settings