Complementing global and local contexts in representing API descriptions to improve API retrieval tasks.

When being trained on API documentation and tutorials, Word2vec produces vector representations to estimate the relevance between texts and API elements. However, existing Word2vec-based approaches to measure document similarities aggregate Word2vec vectors of individual words or APIs to build the representation of a document as if the words are independent. Thus, the semantics of API descriptions or code fragments are not well represented. In this work, we introduce D2Vec, a new model that fits with API documentation better than Word2vec. D2Vec is a neural network model that considers two complementary contexts to better capture the semantics of API documentation. We first connect the global context of the current API topic under description to all the text phrases within the description of that API. Second, the local orders of words and API elements in the text phrases are maintained in computing the vector representations for the APIs. We conducted an experiment to verify two intrinsic properties of D2Vec's vectors: 1) similar words and relevant API elements are projected into nearby locations; and 2) some vector operations carry semantics. We demonstrate the usefulness and good performance of D2Vec in three applications: API code search (text-to-code retrieval), API tutorial fragment search (code-to-text retrieval), and mining API mappings between software libraries (code-to-code retrieval). Finally, we provide actionable insights and implications for researchers in using our model in other applications with other types of documents.