Executive summary
BarrierScan tested 6 pages of barrierscan.com against the WCAG-based axe-core ruleset. The scan identified 0 types of accessibility barrier affecting 0 element instances across the site. Findings below are ordered by priority: the issue patterns most frequently cited in digital-accessibility litigation come first, weighted by severity and by how prominently the affected pages figure in a typical customer journey.
No accessibility barriers were detected by the automated ruleset on the scanned pages. Manual testing is still recommended; see the methodology section.
Severity mix
| Severity | Barrier types | Element instances |
|---|---|---|
| Critical | 0 | 0 |
| Serious | 0 | 0 |
| Moderate | 0 | 0 |
| Minor | 0 | 0 |
Methodology and scope
Pages were discovered from the site's sitemap where available, otherwise by crawling same-origin links from the start URL, respecting robots.txt. Each page was loaded in a current headless Chromium browser at a 1280x800 desktop viewport and evaluated with axe-core, the most widely used open-source accessibility rules engine, against WCAG 2.x A and AA success criteria. Issues are grouped by type: each barrier type appears once, with the distinct page components it affects nested under it and counts of affected pages and element instances.
Prioritization weights each finding by three factors: whether the underlying issue pattern appears among those most frequently cited in digital-accessibility lawsuits (image alternative text, form labels, link and button names, keyboard accessibility, color contrast, media captions, and document language), the severity assigned by the rules engine, and whether the affected pages are critical to the customer journey (home, product, cart, checkout, and contact pages).
Coverage measurements for automated testing: Deque's analysis of roughly 300,000 findings from real audits attributes 57 percent of issue volume to automated rules (deque.com/automated-accessibility-coverage-report). The UK Government Digital Service's planted-barrier experiment found the best single automated tool detected about 41 percent of 143 known barriers (accessibility.blog.gov.uk, 2017). The barrier types most frequently cited in digital-accessibility litigation fall largely within what automated testing detects reliably, which is why this report prioritizes them.