Article

Boreas: an accurate and scalable token-based approach to code clone detection

Authors:

Yang Yuan,

Yao GuoAuthors Info & Claims

ASE '12: Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering

Pages 286 - 289

https://doi.org/10.1145/2351676.2351725

Published: 03 September 2012 Publication History

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Abstract

Detecting code clones in a program has many applications in software engineering and other related fields. In this paper, we present Boreas, an accurate and scalable token-based approach for code clone detection. Boreas introduces a novel counting-based method to define the characteristic matrices, which are able to describe the program segments distinctly and effectively for the purpose of clone detection. We conducted experiments on JDK 7 and Linux kernel 2.6.38.6 source code. Experimental results show that Boreas is able to match the detecting accuracy of a recently proposed syntactic-based tool Deckard, with the execution time reduced by more than an order of magnitude.

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Cited By

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Chen QYu CLiu RZhang CWang YWang KSu TWang L(2024)Evaluating the Effectiveness of Deep Learning Models for Foundational Program Analysis TasksProceedings of the ACM on Programming Languages10.1145/36498298:OOPSLA1(500-528)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3649829
Sheneamer A(2024)A Similarity-based Stacked Deep Learning Architectures for Detection of Software Clones2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)10.1109/ICECCME62383.2024.10796546(1-6)Online publication date: 4-Nov-2024
https://doi.org/10.1109/ICECCME62383.2024.10796546
Jimenez SRakic GTakahashi SCardozo N(2024)Out of Step: Code Clone Detection for Mobile Apps Across Different Language CodebasesScience of Computer Programming10.1016/j.scico.2024.103112(103112)Online publication date: Apr-2024
https://doi.org/10.1016/j.scico.2024.103112
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Index Terms

Boreas: an accurate and scalable token-based approach to code clone detection
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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Information

Published In

ASE '12: Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering

September 2012

409 pages

ISBN:9781450312042

DOI:10.1145/2351676

General Chair:
Michael Goedicke,
Program Chairs:
Tim Menzies,
Motoshi Saeki

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

SIGAI: ACM Special Interest Group on Artificial Intelligence
Universität Duisburg Essen: Universität Duisburg Essen
TCSE: IEEE Computer Society's Tech. Council on Software Engin.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 September 2012

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ASE'12

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SIGSOFT

ASE'12: IEEE/ACM International Conference on Automated Software Engineering

September 3 - 7, 2012

Essen, Germany

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Overall Acceptance Rate 82 of 337 submissions, 24%

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Cited By

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Chen QYu CLiu RZhang CWang YWang KSu TWang L(2024)Evaluating the Effectiveness of Deep Learning Models for Foundational Program Analysis TasksProceedings of the ACM on Programming Languages10.1145/36498298:OOPSLA1(500-528)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3649829
Sheneamer A(2024)A Similarity-based Stacked Deep Learning Architectures for Detection of Software Clones2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)10.1109/ICECCME62383.2024.10796546(1-6)Online publication date: 4-Nov-2024
https://doi.org/10.1109/ICECCME62383.2024.10796546
Jimenez SRakic GTakahashi SCardozo N(2024)Out of Step: Code Clone Detection for Mobile Apps Across Different Language CodebasesScience of Computer Programming10.1016/j.scico.2024.103112(103112)Online publication date: Apr-2024
https://doi.org/10.1016/j.scico.2024.103112
Fang YZhou FXu YLiu Z(2023)TCCCD: Triplet-Based Cross-Language Code Clone DetectionApplied Sciences10.3390/app13211208413:21(12084)Online publication date: 6-Nov-2023
https://doi.org/10.3390/app132112084
Alhazami ESheneamer A(2023)Graph-of-Code: Semantic Clone Detection Using Graph FingerprintsIEEE Transactions on Software Engineering10.1109/TSE.2023.3276780(1-18)Online publication date: 2023
https://doi.org/10.1109/TSE.2023.3276780
Sun YGerman DZacchiroli S(2023)Using the uniqueness of global identifiers to determine the provenance of Python software source codeEmpirical Software Engineering10.1007/s10664-023-10317-828:5Online publication date: 20-Jul-2023
https://doi.org/10.1007/s10664-023-10317-8
Schneider JLee S(2022)An Experimental Comparison of Clone Detection Techniques using Java Bytecode2022 29th Asia-Pacific Software Engineering Conference (APSEC)10.1109/APSEC57359.2022.00026(139-148)Online publication date: Dec-2022
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Karakatič SMiloševič AHeričko T(2022)Software system comparison with semantic source code embeddingsEmpirical Software Engineering10.1007/s10664-022-10122-927:3Online publication date: 17-Mar-2022
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Chen CZain AZhou K(2022)Definition, approaches, and analysis of code duplication detection (2006–2020): a critical reviewNeural Computing and Applications10.1007/s00521-022-07707-234:23(20507-20537)Online publication date: 1-Dec-2022
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Gupta AGoyal R(2022)A Study on the Metrics-Based Duplicated Code Type Smell Detection Techniques Relating the Metrics to Its QualityInventive Communication and Computational Technologies10.1007/978-981-19-4960-9_41(515-532)Online publication date: 14-Nov-2022
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