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LSTM-Based with Deterministic Negative Sampling for API Suggestion.

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Bibliographic Details
Title: LSTM-Based with Deterministic Negative Sampling for API Suggestion.
Authors: Yan, Jinpei, Qi, Yong, Rao, Qifan, He, Hui, Qi, Saiyu
Source: International Journal of Software Engineering & Knowledge Engineering; Jul2019, Vol. 29 Issue 7, p1029-1051, 23p
Subject Terms: SHORT-term memory
Abstract: Modern programming relies on a large number of fundamental APIs, but programmers often take great effort to remember names and the usage of APIs when coding, and repeatedly search the related API documents or Q&A websites (e.g. Stack Overflow). To improve the programming efficiency, we present a Java API suggestion model called APIHelper which learns API sequence pattern via the Long Short-Term Memory (LSTM) network, then provides API suggestion based on the program context. Comparing with statistical methods (e.g. Hidden Markov Model (HMM), N-gram), which require establishing one specific model for each class, we propose Deterministic Negative Sampling (DNS) to make API suggestion for a large number of Java classes by one single end-to-end LSTM. To verify this approach, we make API suggestion for 50,000 Java classes and evaluate it with accuracy and top-K accuracy. The results show that APIHelper outperforms other research works both on accuracy and computation efficiency. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:Modern programming relies on a large number of fundamental APIs, but programmers often take great effort to remember names and the usage of APIs when coding, and repeatedly search the related API documents or Q&A websites (e.g. Stack Overflow). To improve the programming efficiency, we present a Java API suggestion model called APIHelper which learns API sequence pattern via the Long Short-Term Memory (LSTM) network, then provides API suggestion based on the program context. Comparing with statistical methods (e.g. Hidden Markov Model (HMM), N-gram), which require establishing one specific model for each class, we propose Deterministic Negative Sampling (DNS) to make API suggestion for a large number of Java classes by one single end-to-end LSTM. To verify this approach, we make API suggestion for 50,000 Java classes and evaluate it with accuracy and top-K accuracy. The results show that APIHelper outperforms other research works both on accuracy and computation efficiency. [ABSTRACT FROM AUTHOR]
ISSN:02181940
DOI:10.1142/S0218194019500347