  We consider the problem of developing suitable learning representations
(embeddings) for library packages that capture semantic similarity among
libraries. Such representations are known to improve the performance of
downstream learning tasks (e.g. classification) or applications such as
contextual search and analogical reasoning.
  We apply word embedding techniques from natural language processing (NLP) to
train embeddings for library packages ("library vectors"). Library vectors
represent libraries by similar context of use as determined by import
statements present in source code. Experimental results obtained from training
such embeddings on three large open source software corpora reveals that
library vectors capture semantically meaningful relationships among software
libraries, such as the relationship between frameworks and their plug-ins and
libraries commonly used together within ecosystems such as big data
infrastructure projects (in Java), front-end and back-end web development
frameworks (in JavaScript) and data science toolkits (in Python).
