TagNN: A Code Tag Generation Technology for Resource Retrieval from Open‐Source Big Data
Lingbin Zeng, Xin Guo, Cheng Yang, Yao Lu, Xiao Li · Wireless Communications and Mobile Computing · 2021
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.