research-article

Source code authorship approaches natural language processing

Authors:

Juraj Petrík,

Daniela ChudáAuthors Info & Claims

CompSysTech '18: Proceedings of the 19th International Conference on Computer Systems and Technologies

Pages 58 - 61

https://doi.org/10.1145/3274005.3274031

Published: 13 September 2018 Publication History

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Abstract

This paper proposed method for source code authorship attribution using modern natural language processing methods. Our method based on text embedding with convolutional recurrent neural network reaches 94.5% accuracy within 500 authors in one dataset, which outperformed many state of the art models for authorship attribution. Our approach is dealing with source code as with natural language texts, so it is potentially programming language independent with more potential of future improving.

References

[1]

Amancio, D.R. 2015. A complex network approach to stylometry. PLoS ONE. 10, 8 (2015), 1--21.

Crossref

Google Scholar

[2]

Baayen, H. et al. 2002. An experiment in authorship attribution. 6th JADT. I, January (2002), 69--75.

Google Scholar

[3]

Backer, E. and Kranenburg, P. Van 2005. On musical stylometry-a pattern recognition approach. Pattern Recognition Letters. 26, 3 (2005), 299--309.

Digital Library

Google Scholar

[4]

Dauber, E. et al. 2017. Git Blame Who?: Stylistic Authorship Attribution of Small, Incomplete Source Code Fragments. (2017).

Google Scholar

[5]

Duric, Z. and Gasevic, D. 2013. A Source Code Similarity System for Plagiarism Detection. The Computer Journal. 56, 1 (2013), 70--86.

Digital Library

Google Scholar

[6]

Foley, S.N. et al. 2013. Computer Security -- ESORICS 2013.

Google Scholar

[7]

Hutchison, D. 2015. Brief Announcement: A Consent Management Solution for Enterprises.

Google Scholar

[8]

Lantz, J.E. 1943. A survey of modern preaching. Quarterly Journal of Speech. 29, 2 (1943), 167--172.

Crossref

Google Scholar

[9]

Neal, T. et al. 2017. Surveying Stylometry Techniques and Applications. ACM Comput. Surv. Article. 50, 86 (2017).

Digital Library

Google Scholar

[10]

Swain, S. et al. 2017. Recent Approaches on Authorship Attribution Techniques -- An Overview. iii (2017), 557--566.

Google Scholar

[11]

Yang, X. et al. 2017. Authorship attribution of source code by using back propagation neural network based on particle swarm optimization. PLoS ONE. 12, 11 (2017).

Google Scholar

[12]

Young, T. et al. 2017. Recent Trends in Deep Learning Based Natural Language Processing. (2017), 1--24.

Google Scholar

[13]

Zhou, P. et al. 2016. Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). (2016), 207--212.

Crossref

Google Scholar

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Bogdanova AFarina MKholmatova ZKruglov ARomanov VSucci G(2022)Analysis of Source Code Authorship Attribution Problem2022 International Conference on Computers and Artificial Intelligence Technologies (CAIT)10.1109/CAIT56099.2022.10072266(109-115)Online publication date: 4-Nov-2022
https://doi.org/10.1109/CAIT56099.2022.10072266
Bogdanova ARajan H(2021)Source code authorship attribution using file embeddingsCompanion Proceedings of the 2021 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity10.1145/3484271.3484981(31-33)Online publication date: 17-Oct-2021
https://dl.acm.org/doi/10.1145/3484271.3484981

Index Terms

Source code authorship approaches natural language processing
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language features
        Patterns

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CompSysTech '18: Proceedings of the 19th International Conference on Computer Systems and Technologies

September 2018

206 pages

ISBN:9781450364256

DOI:10.1145/3274005

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

ERSVB: EURORISC SYSTEMS - Varna, Bulgaria
FOSEUB: FEDERATION OF THE SCIENTIFIC ENGINEERING UNIONS - Bulgaria
UORB: University of Ruse, Bulgaria
TECHUVB: Technical University of Varna, Bulgaria

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 September 2018

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Scientific Grant Agency of the Slovak Republic, grant No. VG 1/0646/15
Scientific Grant Agency of the Slovak Republic, grant No. VG 1/0667/18
Slovak Research and Development Agency under the contract No. APVV-17-0267 - Automated Recognition of Antisocial Behaviour in Online Communities

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CompSysTech'18

CompSysTech'18: 19th International Conference on Computer Systems and Technologies

September 13 - 14, 2018

Ruse, Bulgaria

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Overall Acceptance Rate 241 of 492 submissions, 49%

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Bogdanova AFarina MKholmatova ZKruglov ARomanov VSucci G(2022)Analysis of Source Code Authorship Attribution Problem2022 International Conference on Computers and Artificial Intelligence Technologies (CAIT)10.1109/CAIT56099.2022.10072266(109-115)Online publication date: 4-Nov-2022
https://doi.org/10.1109/CAIT56099.2022.10072266
Bogdanova ARajan H(2021)Source code authorship attribution using file embeddingsCompanion Proceedings of the 2021 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity10.1145/3484271.3484981(31-33)Online publication date: 17-Oct-2021
https://dl.acm.org/doi/10.1145/3484271.3484981

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Cited By

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Recommendations

RoPGen: towards robust code authorship attribution via automatic coding style transformation

The effect of time drift in source code authorship attribution: Time drifting in source code - stylochronometry

Deep Learning vs. Conventional Approaches in Stylometry: An Authorship Attribution Study

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