kirancodes.me
To Proof Maintenance & Beyond!
FSE 2022

CodeMatcher: a tool for large-scale code search based on query semantics matching

Chao Liu, Xuanlin Bao, Xin Xia, Meng Yan, David Lo, Ting Zhang

PDFDOI 10.1145/3540250.3558935dblp

Abstract

Due to the emergence of large-scale codebases, such as GitHub and Gitee, searching and reusing existing code can help developers substantially improve software development productivity. Over the years, many code search tools have been developed. Early tools leveraged the information retrieval (IR) technique to perform an efficient code search for a frequently changed large-scale codebase. However, the search accuracy was low due to the semantic mismatch between query and code. In the recent years, many tools leveraged Deep Learning (DL) technique to address this issue. But the DL-based tools are slow and the search accuracy is unstable.

BibTeX
@inproceedings{Liu-al:FSE22,
  author    = {Chao Liu and
               Xuanlin Bao and
               Xin Xia and
               Meng Yan and
               David Lo and
               Ting Zhang},
  title     = {{CodeMatcher:} a tool for large-scale code search based on query semantics matching},
  booktitle = {{ESEC/SIGSOFT} {FSE}},
  pages     = {1642--1646},
  publisher = {{ACM}},
  year      = {2022},
}

Related papers