  Developers often wonder how to implement a certain functionality (e.g., how
to parse XML files) using APIs. Obtaining an API usage sequence based on an
API-related natural language query is very helpful in this regard. Given a
query, existing approaches utilize information retrieval models to search for
matching API sequences. These approaches treat queries and APIs as bag-of-words
(i.e., keyword matching or word-to-word alignment) and lack a deep
understanding of the semantics of the query.
  We propose DeepAPI, a deep learning based approach to generate API usage
sequences for a given natural language query. Instead of a bags-of-words
assumption, it learns the sequence of words in a query and the sequence of
associated APIs. DeepAPI adapts a neural language model named RNN
Encoder-Decoder. It encodes a word sequence (user query) into a fixed-length
context vector, and generates an API sequence based on the context vector. We
also augment the RNN Encoder-Decoder by considering the importance of
individual APIs. We empirically evaluate our approach with more than 7 million
annotated code snippets collected from GitHub. The results show that our
approach generates largely accurate API sequences and outperforms the related
approaches.
