O que a automação consegue e não consegue detectar

Automated tools scan your page and flag likely problems in seconds. They are a powerful first pass, but they cannot tell you if a page truly works for real people.

Studies suggest automated tools catch only around 30 percent of accessibility issues. The other seventy percent still need a human to find them.

Tools excel at mechanical checks: missing alt attributes, empty buttons, low contrast ratios, and duplicate id values. These rules are easy to verify with code.

An image with no alt attribute is a clear, machine-detectable miss. A scanner flags this instantly because the rule is simply yes or no.

A tool sees that alt text exists, but not whether it is meaningful. The word image as alt passes the check yet helps no one.

Is the link text clear out of context? Is the reading order logical? Does the error message actually help? These judgments need a person.

Sometimes a tool flags something that is actually fine. Treat each result as a question to investigate, not an automatic verdict of failure.

A green score does not mean accessible. A page can pass every automated rule and still be a maze for a keyboard or screen reader user.

Think of automation as a spell checker. It catches obvious typos fast, but it never tells you whether your writing is any good.

Its real superpower is speed at scale. Run it on every commit and it guards thousands of pages against the easy regressions humans miss.

The best strategy pairs automation with manual testing: let tools clear the low-hanging fruit so your human time goes to the hard, high-value checks.

Think about what a scanner can really verify.

Automation finds the easy wins fast but tops out near thirty percent. Pair it with human testing, and never mistake a passing score for an accessible page. 🤝