  Developers increasingly rely on text matching tools to analyze the relation
between natural language words and APIs. However, semantic gaps, namely textual
mismatches between words and APIs, negatively affect these tools. Previous
studies have transformed words or APIs into low-dimensional vectors for
matching; however, inaccurate results were obtained due to the failure of
modeling words and APIs simultaneously. To resolve this problem, two main
challenges are to be addressed: the acquisition of massive words and APIs for
mining and the alignment of words and APIs for modeling. Therefore, this study
proposes Word2API to effectively estimate relatedness of words and APIs.
Word2API collects millions of commonly used words and APIs from code
repositories to address the acquisition challenge. Then, a shuffling strategy
is used to transform related words and APIs into tuples to address the
alignment challenge. Using these tuples, Word2API models words and APIs
simultaneously. Word2API outperforms baselines by 10%-49.6% of relatedness
estimation in terms of precision and NDCG. Word2API is also effective on
solving typical software tasks, e.g., query expansion and API documents
linking. A simple system with Word2API-expanded queries recommends up to 21.4%
more related APIs for developers. Meanwhile, Word2API improves comparison
algorithms by 7.9%-17.4% in linking questions in Question&Answer communities to
API documents.
