Wurfl - overview

Here you find the details of this provider

We analyzed 6136 user agents
Group Percent Total Actions
Results found 93.9 %
5762 Not found
Browser found 75.26 %
4618 Detected names Not found
Engine found 0 %
0 Detected names Not found
Operating system found 78.91 %
4842 Detected names Not found
Device detected 91.09 %
5589
Device model found 82.99 %
5092 Detected models
Device brand found 82.99 %
5092 Detected brands
Device type found 91.09 %
5589 Detected types
As mobile detected 72.49 %
4448
As bot detected 53.89 %
173 Not detected as bot
Bot name found 0 %
0
Bot type found 0 %
0

I'm done here

More informations

The primary goal of this project is simple. I wanted to know which user agent parser is the most accurate in each part - device detection, bot detection and so on...

The secondary goal is to provide a source for all user agent parsers to improve their detection based on this results.

You can also improve this further, by suggesting ideads at ThaDafinser/UserAgentParserComparison

The comparison is based on the abstraction by ThaDafinser/UserAgentParser
Comparison created 2015-11-20 13:30:58 | by ThaDafinser