  Programming language documentation refers to the set of technical documents
that provide application developers with a description of the high-level
concepts of a language. Such documentation is essential to support application
developers in the effective use of a programming language. One of the
challenges faced by documenters (i.e., personnel that produce documentation) is
to ensure that documentation has relevant information that aligns with the
concrete needs of developers. In this paper, we present an automated approach
to support documenters in evaluating the differences and similarities between
the concrete information need of developers and the current state of
documentation (a problem that we refer to as the topical alignment of a
programming language documentation). Our approach leverages semi-supervised
topic modelling to assess the similarities and differences between the topics
of Q&A posts and the official documentation. To demonstrate the application of
our approach, we perform a case study on the documentation of Rust. Our results
show that there is a relatively high level of topical alignment in Rust
documentation. Still, information about specific topics is scarce in both the
Q&A websites and the documentation, particularly related topics with
programming niches such as network, game, and database development. For other
topics (e.g., related topics with language features such as structs, patterns
and matchings, and foreign function interface), information is only available
on Q&A websites while lacking in the official documentation. Finally, we
discuss implications for programming language documenters, particularly how to
leverage our approach to prioritize topics that should be added to the
documentation.
