research-article

FaCoY: a code-to-code search engine

Authors:

Tegawendé F. Bissyandé,

Yves Le TraonAuthors Info & Claims

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

Pages 946 - 957

https://doi.org/10.1145/3180155.3180187

Published: 27 May 2018 Publication History

Abstract

Code search is an unavoidable activity in software development. Various approaches and techniques have been explored in the literature to support code search tasks. Most of these approaches focus on serving user queries provided as natural language free-form input. However, there exists a wide range of use-case scenarios where a code-to-code approach would be most beneficial. For example, research directions in code transplantation, code diversity, patch recommendation can leverage a code-to-code search engine to find essential ingredients for their techniques. In this paper, we propose FaCoY, a novel approach for statically finding code fragments which may be semantically similar to user input code. FaCoY implements a query alternation strategy: instead of directly matching code query tokens with code in the search space, FaCoY first attempts to identify other tokens which may also be relevant in implementing the functional behavior of the input code. With various experiments, we show that (1) FaCoY is more effective than online code-to-code search engines; (2) FaCoY can detect more semantic code clones (i.e., Type-4) in BigCloneBench than the state-of-the-art; (3) FaCoY, while static, can detect code fragments which are indeed similar with respect to runtime execution behavior; and (4) FaCoY can be useful in code/patch recommendation.

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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
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Program Chairs:
Marsha Chechik
University of Toronto, Canada
,
Mark Harman
Facebook and University College London, United Kingdom

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May 27 - June 3, 2018

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https://dl.acm.org/doi/10.1145/3663529.3663848
Chen BGolebiowski JAbedjan ZSpinellis DConstantinou EBacchelli A(2024)Data Augmentation for Supervised Code Translation LearningProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644923(444-456)Online publication date: 15-Apr-2024
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