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A11yLTLNav: Automatic Detection of Accessibility Navigation Failures — arXiv:2609.17959

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.17959
License (as archived): CC0-1.0
Archived: 2026-10-15T14:40:59.820Z
SHA-256 of the archived text: 8b4cb9ddf2ce146e9285a366a0e751a62ed8fb3ad0442794af309e698e2f86c1
Stable URL of this page: https://metraipolis.com/documento/dc_8ea446ce3468

How to cite

A11yLTLNav: Automatic Detection of Accessibility Navigation Failures — arXiv:2609.17959 — arXiv (en), https://arxiv.org/abs/2609.17959 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_8ea446ce3468 (Metraipolis archive dc_8ea446ce3468, sha256 8b4cb9ddf2ce146e9285a366a0e751a62ed8fb3ad0442794af309e698e2f86c1)

BibTeX

@misc{dc8ea446ce3468,
  title = {A11yLTLNav: Automatic Detection of Accessibility Navigation Failures — arXiv:2609.17959},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.17959},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_8ea446ce3468; sha256 8b4cb9ddf2ce146e9285a366a0e751a62ed8fb3ad0442794af309e698e2f86c1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.17959. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

For blind and low-vision (BLV) screen-reader users, a website that appears accessible in a static snapshot can become difficult or impossible to navigate once interaction begins. Yet, most automated accessibility checkers miss failures involving focus, interface state, and accessible feedback across interactions. We present A11yLTLNav, a property-based approach for automatically detecting accessibility navigation failures. Through a structured review of prior research, we organize accessibility navigation failures into a failure taxonomy and formalize a browser-observable subset as executable Linear Temporal Logic properties over action-state traces. A11yLTLNav combines random keyboard exploration with runtime property monitoring to detect these failures during interactions. We evaluate A11yLTLNav on 31 generated websites based on real-world websites and tasks. It reported 309 accessibility failures, of which 274 were confirmed, achieving 88.7% precision and identifying more confirmed failures than the comparison checkers. Our results show that A11yLTLNav transforms accessibility knowledge into reusable checks of interface behavior over time.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-17 in cs.HC).
Categorie arXiv: Human-Computer Interaction (cs.HC) ; Software Engineering (cs.SE).
Pagina del lavoro: https://arxiv.org/abs/2609.17959 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T14:00:40.621Z dalla pagina https://arxiv.org/catchup/cs.HC/2026-09-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).