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Cited by 485

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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

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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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
OpenAlex
W2402619042
Cited by
485
References
7
FWCI
49.29
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Deep API learning. https://app.cochat.ai/papers/10.1145/2950290.2950334
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Gu, X., Zhang, H., Zhang, D., & Kim, S. (2016). Deep API learning. Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering. https://doi.org/10.1145/2950290.2950334

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