  Question and answer (Q&A) forums contain valuable information regarding
software reuse, but they can be challenging to analyse due to their
unstructured free text. Here we introduce a new approach (LANLAN), using word
embeddings and machine learning, to harness information available in
StackOverflow. Specifically, we consider two different kinds of user
communication describing difficulties encountered in software reuse: 'problem
reports' point to potential defects, while 'support requests' ask for
clarification on software usage. Word embeddings were trained on 1.6 billion
tokens from StackOverflow and applied to identify which Q&A forum messages
(from two large open source projects: Eclipse and Bioconductor) correspond to
problem reports or support requests. LANLAN achieved an area under the receiver
operator curve (AUROC) of over 0.9; it can be used to explore the relationship
between software reusability metrics and difficulties encountered by users, as
well as predict the number of difficulties users will face in the future. Q&A
forum data can help improve understanding of software reuse, and may be
harnessed as an additional resource to evaluate software reusability metrics.
