Accelerating Code Search with Deep Hashing and Code Classification
Wenchao Gu, Yanlin Wang, Lun Du, Hongyu Zhang, Shi Han, Dongmei Zhang, Michael Lyu
Abstract
Code search is to search reusable code snippets from source code corpus based on natural languages queries. Deep learning-based methods on code search have shown promising results. However, previous methods focus on retrieval accuracy, but lacked attention to the efficiency of the retrieval process. We propose a novel method CoSHC to accelerate code search with deep hashing and code classification, aiming to perform efficient code search without sacrificing too much accuracy. To evaluate the effectiveness of CoSHC, we apply our methodon five code search models. Extensive experimental results indicate that compared with previous code search baselines, CoSHC can save more than 90% of retrieval time meanwhile preserving at least 99% of retrieval accuracy.
BibTeX
@inproceedings{gu-etal-2022-accelerating,
title = "Accelerating Code Search with Deep Hashing and Code Classification",
author = "Gu, Wenchao and
Wang, Yanlin and
Du, Lun and
Zhang, Hongyu and
Han, Shi and
Zhang, Dongmei and
Lyu, Michael",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.181/",
doi = "10.18653/v1/2022.acl-long.181",
pages = "2534--2544"
}