# Towards Automated Accessibility Report Generation for Mobile Apps

arXiv: https://arxiv.org/abs/2310.00091 · HTML: https://mlsanity.com/paper/2310.00091/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2023.

## Built on by (most cited first, 5 of 5)

- [UIClip: A Data-driven Model for Assessing User Interface Design](https://mlsanity.com/paper/2404.12500/) (2024)
- [From Interaction to Impact: Towards Safer AI Agents Through Understanding and Evaluating Mobile UI Operation Impacts](https://mlsanity.com/paper/2410.09006/) (2024)
- [Automated Code Fix Suggestions for Accessibility Issues in Mobile Apps](https://mlsanity.com/paper/2408.03827/) (2024)
- [ScreenAudit: Detecting Screen Reader Accessibility Errors in Mobile Apps Using Large Language Models](https://mlsanity.com/paper/2504.02110/) (2025)
- [Sidekick: Designing Communication for Effective Multitasking with Computer Use Agents](https://mlsanity.com/paper/2607.17527/) (2026)

## Abstract (arXiv)

Many apps have basic accessibility issues, like missing labels or low contrast. Automated tools can help app developers catch basic issues, but can be laborious or require writing dedicated tests. We propose a system, motivated by a collaborative process with accessibility stakeholders at a large technology company, to generate whole app accessibility reports by combining varied data collection methods (e.g., app crawling, manual recording) with an existing accessibility scanner. Many such scanners are based on single-screen scanning, and a key problem in whole app accessibility reporting is to effectively de-duplicate and summarize issues collected across an app. To this end, we developed a screen grouping model with 96.9% accuracy (88.8% F1-score) and UI element matching heuristics with 97% accuracy (98.2% F1-score). We combine these technologies in a system to report and summarize unique issues across an app, and enable a unique pixel-based ignore feature to help engineers and testers better manage reported issues across their app's lifetime. We conducted a qualitative evaluation with 18 accessibility-focused engineers and testers which showed this system can enhance their existing accessibility testing toolkit and address key limitations in current accessibility scanning tools.