  Programming languages themselves have a limited number of reserved keywords
and character based tokens that define the language specification. However,
programmers have a rich use of natural language within their code through
comments, text literals and naming entities. The programmer defined names that
can be found in source code are a rich source of information to build a high
level understanding of the project. The goal of this paper is to apply topic
modeling to names used in over 13.6 million repositories and perceive the
inferred topics. One of the problems in such a study is the occurrence of
duplicate repositories not officially marked as forks (obscure forks). We show
how to address it using the same identifiers which are extracted for topic
modeling.
  We open with a discussion on naming in source code, we then elaborate on our
approach to remove exact duplicate and fuzzy duplicate repositories using
Locality Sensitive Hashing on the bag-of-words model and then discuss our work
on topic modeling; and finally present the results from our data analysis
together with open-access to the source code, tools and datasets.
