research-article

Deep code search

Authors:

Sunghun KimAuthors Info & Claims

ICSE '18: Proceedings of the 40th International Conference on Software Engineering

Pages 933 - 944

https://doi.org/10.1145/3180155.3180167

Published: 27 May 2018 Publication History

Abstract

To implement a program functionality, developers can reuse previously written code snippets by searching through a large-scale codebase. Over the years, many code search tools have been proposed to help developers. The existing approaches often treat source code as textual documents and utilize information retrieval models to retrieve relevant code snippets that match a given query. These approaches mainly rely on the textual similarity between source code and natural language query. They lack a deep understanding of the semantics of queries and source code.

In this paper, we propose a novel deep neural network named CODEnn (Code-Description Embedding Neural Network). Instead of matching text similarity, CODEnn jointly embeds code snippets and natural language descriptions into a high-dimensional vector space, in such a way that code snippet and its corresponding description have similar vectors. Using the unified vector representation, code snippets related to a natural language query can be retrieved according to their vectors. Semantically related words can also be recognized and irrelevant/noisy keywords in queries can be handled.

As a proof-of-concept application, we implement a code search tool named DeepCS using the proposed CODEnn model. We empirically evaluate DeepCS on a large scale codebase collected from GitHub. The experimental results show that our approach can effectively retrieve relevant code snippets and outperforms previous techniques.

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Index Terms

Deep code search
1. Software and its engineering
  1. Software creation and management
    1. Software development techniques
      1. Reusability

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Graphical abstract

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cover image ACM Conferences

ICSE '18: Proceedings of the 40th International Conference on Software Engineering

May 2018

1307 pages

ISBN:9781450356381

DOI:10.1145/3180155

Conference Chair:
Michel Chaudron
Chalmers University of Technology, University of Gothenburg, Sweden
,
General Chair:
Ivica Crnkovic
Chalmers University of Technology, University of Gothenburg, Sweden
,
Program Chairs:
Marsha Chechik
University of Toronto, Canada
,
Mark Harman
Facebook and University College London, United Kingdom

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Published: 27 May 2018

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ICSE '18: 40th International Conference on Software Engineering

May 27 - June 3, 2018

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https://dl.acm.org/doi/10.1109/TSE.2024.3504286
Guo ZShang QLi XLi CZhang ZZhang ZHu JAn JHuang CChen YCai Y(2025)Web-FTP: A Feature Transferring-Based Pre-Trained Model for Web Attack DetectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.351279337:3(1495-1507)Online publication date: Mar-2025
https://doi.org/10.1109/TKDE.2024.3512793
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