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Facial recognition systems are generally utterly broken. Other examples of this:

- user-presence detection systems that can't see you if you have dark skin (Apple FaceID has/had this problem)

- the high rate of false positives with the facial recognition systems police use at protests

- driver drowsiness detection that can't tell the difference between a sleepy driver and someone with a narrow eye shape


Do you have a link to a copy of the video with captions? YouTube autogen doesn't cut it unfortunately. Or perhaps a written-form version (slide deck + transcript)?

What's in the 8% not covered by testing?


I don't have a version with captions, sorry. You can find the slidedeck at https://github.com/elves/elvish/blob/master/website/slides/2...

The remaining 8% mostly falls into the following categories:

- Code that use OS functionalities that are cumbersome to mock in tests

- Code paths that are triggered relatively rarely and I was simply too lazy to add tests for them

Nothing is impossible to cover, but for whatever reason it was too much work for me when I wrote the code.

However, it's worth mentioning that I only settled on the transcript test pattern fairly recently, and if I were to rewrite or refactor some of the untested code today I would add tests for them, because the cost of adding tests has been lowered considerably. So Elvish's test coverage is still increasing slowly as the cost of testing decreases.


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