  README files play an essential role in shaping a developer's first impression
of a software repository and in documenting the software project that the
repository hosts. Yet, we lack a systematic understanding of the content of a
typical README file as well as tools that can process these files
automatically. To close this gap, we conduct a qualitative study involving the
manual annotation of 4,226 README file sections from 393 randomly sampled
GitHub repositories and we design and evaluate a classifier and a set of
features that can categorize these sections automatically. We find that
information discussing the `What' and `How' of a repository is very common,
while many README files lack information regarding the purpose and status of a
repository. Our multi-label classifier which can predict eight different
categories achieves an F1 score of 0.746. To evaluate the usefulness of the
classification, we used the automatically determined classes to label sections
in GitHub README files using badges and showed files with and without these
badges to twenty software professionals. The majority of participants perceived
the automated labeling of sections based on our classifier to ease information
discovery. This work enables the owners of software repositories to improve the
quality of their documentation and it has the potential to make it easier for
the software development community to discover relevant information in GitHub
README files.
