TagNN: A Code Tag Generation Technology for Resource Retrieval from Open-Source Big Data
Author(s) -
Lingbin Zeng,
Xin Guo,
Cheng Yang,
Yao Lu,
Xiao Li
Publication year - 2021
Publication title -
wireless communications and mobile computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.42
H-Index - 64
eISSN - 1530-8677
pISSN - 1530-8669
DOI - 10.1155/2021/9956207
Subject(s) - computer science , open source , code (set theory) , source code , information retrieval , world wide web , resource (disambiguation) , database , programming language , software , set (abstract data type) , computer network
With the vigorous development of open-source software, a huge number of open-source projects and open-source codes have been accumulated in open-source big data, which contains a wealth of code resources. However, effectively and efficiently retrieving the relevant code snippets in such a large amount of open-source big data is an extremely difficult problem. There are usually large gaps between the user’s natural language description and the open-source code snippets. In this paper, we propose a novel code tag generation and code retrieval approach named TagNN, which combines software engineering empirical knowledge and a deep learning algorithm. The experimental results show that our method has good effects on code tag generation and code snippet retrieval.
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