.png){kind=icon}
返回
Fitting missing API puzzles with machine translation techniques
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2022.119477.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
While implementing software projects, developers do not reinvent the wheel but try to reuse existing API calls and source code. In recent years, the problems related to recommending APIs and code snippets have been intensively investigated. Although current approaches have achieved encouraging performance, there is still the need to improve the recommendation process's effectiveness and efficiency. In this work, we reformulate the problem of API recommendations by proposing learning and recommending API sequences relevant to a given coding context. We present LUPE, a novel approach to API and code recommendation, exploiting cutting-edge deep learning techniques. Thanks to the underlying Encoder-Decoder architecture specialized in transforming sequences, LUPE can effectively learn the order in which invocations occur. The approach has been evaluated on two Android datasets and compared with GAPI and FACER, two state-of-the-art API recommender systems. Being fed with augmented training data, our conceived approach can obtain a high prediction accuracy, and produce a perfect match in several cases, hence outperforming the baselines.
Keyword:
API recommendation
Source code recommendation
Encoder-Decoder LSTM
Machine translation
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
The 2.6 Angstrom Crystal Structure of a Human A2AAdenosine Receptor Bound to an Antagonist.png)
Science
OAAI
ScienceIF0
OAAI分享收藏SequenceR: Sequence-to-Sequence Learning for End-to-End Program RepairSequenceR: 用于端到端程序修复的序列到序列学习OAAI
OAAI分享收藏Preparation and characterization of SiO2/TiO2 core-shell particles with controlled shell thickness
PREAI
分享收藏
查看更多内容