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Cited by 485

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Deep API learning

Gu, Xiaodong, Zhang, Hongyu, Zhang, Dongmei, Kim, Sunghun·2016·Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering
DOI 10.1145/2950290.2950334
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Abstract

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 bags-of-words 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 bag-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.

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Cited by

Metadata

Authors
Gu, X., Zhang, H., Zhang, D., & Kim, S.
Venue
Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering
Publisher
ACM
Published
2016-11
Pages
631-642
DOI
10.1145/2950290.2950334
OpenAlex
W2402619042
Cited by
485
References
7
FWCI
49.29
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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