research-article

Deep Graph Matching and Searching for Semantic Code Retrieval

Authors:

Shouling JiAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 15, Issue 5

Article No.: 88, Pages 1 - 21

https://doi.org/10.1145/3447571

Published: 10 May 2021 Publication History

Abstract

Code retrieval is to find the code snippet from a large corpus of source code repositories that highly matches the query of natural language description. Recent work mainly uses natural language processing techniques to process both query texts (i.e., human natural language) and code snippets (i.e., machine programming language), however, neglecting the deep structured features of query texts and source codes, both of which contain rich semantic information. In this article, we propose an end-to-end deep graph matching and searching (DGMS) model based on graph neural networks for the task of semantic code retrieval. To this end, we first represent both natural language query texts and programming language code snippets with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet. In particular, DGMS not only captures more structural information for individual query texts or code snippets, but also learns the fine-grained similarity between them by cross-attention based semantic matching operations. We evaluate the proposed DGMS model on two public code retrieval datasets with two representative programming languages (i.e., Java and Python). Experiment results demonstrate that DGMS significantly outperforms state-of-the-art baseline models by a large margin on both datasets. Moreover, our extensive ablation studies systematically investigate and illustrate the impact of each part of DGMS.

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Li ZLei HMa ZZhang F(2024)Code Similarity Prediction Model for Industrial Management Features Based on Graph Neural NetworksEntropy10.3390/e2606050526:6(505)Online publication date: 9-Jun-2024
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Bibi NMaqbool ARana TAfzal FKhan A(2024)C2B: A Semantic Source Code Retrieval Model Using CodeT5 and Bi-LSTMApplied Sciences10.3390/app1413579514:13(5795)Online publication date: 2-Jul-2024
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Zhang FLi MWu HWu T(2024)Intelligent code search aids edge software developmentJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00629-513:1Online publication date: 1-Apr-2024
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Index Terms

Deep Graph Matching and Searching for Semantic Code Retrieval
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Specialized information retrieval
      1. Multimedia and multimodal retrieval

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 15, Issue 5

October 2021

508 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3461317

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Copyright © 2021 ACM.

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Publication History

Published: 10 May 2021

Accepted: 01 January 2021

Received: 01 September 2020

Published in TKDD Volume 15, Issue 5

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National Key R&D Program of China
Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars
Fundamental Research Funds for the Central Universities (Zhejiang University NGICS Platform)
NSFC
Key R&D Program of Zhejiang Province
Major Scientific Project of Zhejiang Laboratory

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Bibi NMaqbool ARana TAfzal FKhan A(2024)C2B: A Semantic Source Code Retrieval Model Using CodeT5 and Bi-LSTMApplied Sciences10.3390/app1413579514:13(5795)Online publication date: 2-Jul-2024
https://doi.org/10.3390/app14135795
Zhang FLi MWu HWu T(2024)Intelligent code search aids edge software developmentJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00629-513:1Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1186/s13677-024-00629-5
Sun WFang CGe YHu YChen YZhang QGe XLiu YChen Z(2024)A Survey of Source Code Search: A 3-Dimensional PerspectiveACM Transactions on Software Engineering and Methodology10.1145/365634133:6(1-51)Online publication date: 28-Jun-2024
https://dl.acm.org/doi/10.1145/3656341
Wang SGeng MLin BSun ZWen MLiu YLi LBissyandé TMao X(2024)Fusing Code SearchersIEEE Transactions on Software Engineering10.1109/TSE.2024.340304250:7(1852-1866)Online publication date: 1-Jul-2024
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Zhang SShuiyan LRongzhi QZhou X(2024)CodeFuse: Multimodal Code Search Model with Fine-Grained Attention Alignment2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00170(1290-1299)Online publication date: 2-Jul-2024
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Song QHu HDai T(2024)CMCS: contrastive-metric learning via vector-level sampling and augmentation for code searchScientific Reports10.1038/s41598-024-64205-214:1Online publication date: 24-Jun-2024
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Gou QDong YWu YKe Q(2024)RRGcode: Deep hierarchical search-based code generationJournal of Systems and Software10.1016/j.jss.2024.111982211(111982)Online publication date: May-2024
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Swarna KMathews NVagavolu DChimalakonda S(2024)On the impact of multiple source code representations on software engineering tasks — An empirical studyJournal of Systems and Software10.1016/j.jss.2023.111941210:COnline publication date: 25-Jun-2024
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