Bridging the Interpretation Gap in Accessibility Testing: Empathetic and Legal-Aware Bug Report Generation via Large Language Models

2026-03-24

AI Summary

This work addresses the limited accessibility of technical bug reports generated by automated accessibility testing tools, which often hinder comprehension and timely remediation by non-technical stakeholders. To bridge this gap, the authors propose HEAR—a novel framework that integrates empathetic perspectives and legal compliance awareness into accessibility report generation. By leveraging large language models, HEAR transforms raw accessibility logs into narrative reports through UI context reconstruction, injection of personas representing users with disabilities, and multi-layered reasoning. Evaluation on four real-world Android applications demonstrates that HEAR-generated reports preserve factual accuracy while significantly enhancing empathy, perceived urgency, persuasiveness, and awareness of legal risks—all without imposing additional cognitive burden on readers.

Technology Category

• Humans and AI: AI for Accessibility
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Algorithmic accountability and transparency on the web
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Modern automated accessibility testing tools for mobile applications have significantly improved the detection of interface violations, yet their impact on remediation remains limited. A key reason is that existing tools typically produce low-level, technical outputs that are difficult for non-specialist stakeholders, such as product managers and designers, to interpret in terms of real user harm and compliance risk. In this paper, we present \textsc{HEAR} (\underline{H}uman-c\underline{E}ntered \underline{A}ccessibility \underline{R}eporting), a framework that bridges this interpretation gap by transforming raw accessibility bug reports into empathetic, stakeholder-oriented narratives. Given the outputs of the existing accessibility testing tool, \textsc{HEAR} first reconstructs the UI context through semantic slicing and visual grounding, then dynamically injects disability-oriented personas matched to each violation type, and finally performs multi-layer reasoning to explain the physical barrier, functional blockage, and relevant legal or compliance concerns. We evaluate the framework on real-world accessibility issues collected from four popular Android applications and conduct a user study (N=12). The results show that \textsc{HEAR} generates factually grounded reports and substantially improves perceived empathy, urgency, persuasiveness, and awareness of legal risk compared with raw technical logs, while imposing little additional cognitive burden.

Problem

• accessibility testing
• bug report generation
• interpretation gap
• legal compliance
• empathetic communication

Innovation

• Accessibility Testing
• Large Language Models
• Empathetic Reporting
• Legal Compliance
• UI Context Reconstruction