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

TL;DR

This paper introduces HEAR, a framework that transforms technical accessibility bug reports into empathetic, stakeholder-friendly narratives by reconstructing UI context, injecting personas, and reasoning about legal and user impact, improving understanding and engagement.

Contribution

HEAR is a novel framework that enhances accessibility bug reports with empathetic and legal-aware narratives, bridging the interpretation gap for non-specialist stakeholders.

Findings

HEAR improves perceived empathy and urgency in reports.

Generated reports increase awareness of legal risks.

The framework performs well on real-world Android app issues.

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.