A Bayesian ensemble classifier for source code authorship attribution

Matthew F. Tennyson; Francisco J. Mitropoulos

doi:10.1007/978-3-319-11988-5_25

A Bayesian ensemble classifier for source code authorship attribution

Matthew F. Tennyson
, Francisco J. Mitropoulos

Nova Southeastern University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Scopus citations

Abstract

Authorship attribution of source code is the task of deciding who wrote software, given its source code, when the author of the software is not explicitly known. There are numerous scenarios in which it is necessary to identify the author of a piece of software whose author is unknown, including software forensics investigations, plagiarism detection, and questions of software ownership. A number of methods for authorship attribution of source code have been presented in the past, including two state-of-the-art methods: SCAP and Burrows. Each of these two state-of-the-art methods was individually improved, and – as presented in this paper – an ensemble method was developed from them based on the Bayes optimal classifier. An empirical study was performed using a data set consisting of 7,231 open-source and textbook programs written in C++ and Java by thirty unique authors. The ensemble method successfully attributed 98.2% of all documents in the data set, compared to 88.9% by the Burrows baseline method and 91.0% by the SCAP baseline method.

Original language	English
Title of host publication	Similarity Search and Applications - 7th International Conference, SISAP 2014, Proceedings
Editors	Agma Juci Machado Traina, Caetano Traina, Robson Leonardo Ferreira Cordeiro
Publisher	Springer Verlag
Pages	265-276
Number of pages	12
ISBN (Electronic)	9783319119878
DOIs	https://doi.org/10.1007/978-3-319-11988-5_25
State	Published - 2014
Event	7th International Conference on Similarity Search and Applications, SISAP 2014 - Los Cabos, Mexico Duration: Oct 29 2014 → Oct 31 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8821
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Similarity Search and Applications, SISAP 2014
Country/Territory	Mexico
City	Los Cabos
Period	10/29/14 → 10/31/14

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2014.

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Keywords

Authorship attribution
Bayesian probability theory
Information retrieval
Plagiarism detection
Software forensics

Access to Document

10.1007/978-3-319-11988-5_25

Cite this

Tennyson, M. F., & Mitropoulos, F. J. (2014). A Bayesian ensemble classifier for source code authorship attribution. In A. J. M. Traina, C. Traina, & R. L. F. Cordeiro (Eds.), Similarity Search and Applications - 7th International Conference, SISAP 2014, Proceedings (pp. 265-276). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8821). Springer Verlag. https://doi.org/10.1007/978-3-319-11988-5_25

A Bayesian ensemble classifier for source code authorship attribution. / Tennyson, Matthew F.; Mitropoulos, Francisco J.
Similarity Search and Applications - 7th International Conference, SISAP 2014, Proceedings. ed. / Agma Juci Machado Traina; Caetano Traina; Robson Leonardo Ferreira Cordeiro. Springer Verlag, 2014. p. 265-276 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8821).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tennyson, MF & Mitropoulos, FJ 2014, A Bayesian ensemble classifier for source code authorship attribution. in AJM Traina, C Traina & RLF Cordeiro (eds), Similarity Search and Applications - 7th International Conference, SISAP 2014, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8821, Springer Verlag, pp. 265-276, 7th International Conference on Similarity Search and Applications, SISAP 2014, Los Cabos, Mexico, 10/29/14. https://doi.org/10.1007/978-3-319-11988-5_25

Tennyson MF, Mitropoulos FJ. A Bayesian ensemble classifier for source code authorship attribution. In Traina AJM, Traina C, Cordeiro RLF, editors, Similarity Search and Applications - 7th International Conference, SISAP 2014, Proceedings. Springer Verlag. 2014. p. 265-276. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-11988-5_25

Tennyson, Matthew F. ; Mitropoulos, Francisco J. / A Bayesian ensemble classifier for source code authorship attribution. Similarity Search and Applications - 7th International Conference, SISAP 2014, Proceedings. editor / Agma Juci Machado Traina ; Caetano Traina ; Robson Leonardo Ferreira Cordeiro. Springer Verlag, 2014. pp. 265-276 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Authorship attribution of source code is the task of deciding who wrote software, given its source code, when the author of the software is not explicitly known. There are numerous scenarios in which it is necessary to identify the author of a piece of software whose author is unknown, including software forensics investigations, plagiarism detection, and questions of software ownership. A number of methods for authorship attribution of source code have been presented in the past, including two state-of-the-art methods: SCAP and Burrows. Each of these two state-of-the-art methods was individually improved, and {\textendash} as presented in this paper {\textendash} an ensemble method was developed from them based on the Bayes optimal classifier. An empirical study was performed using a data set consisting of 7,231 open-source and textbook programs written in C++ and Java by thirty unique authors. The ensemble method successfully attributed 98.2\% of all documents in the data set, compared to 88.9\% by the Burrows baseline method and 91.0\% by the SCAP baseline method. ",

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A Bayesian ensemble classifier for source code authorship attribution

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

Fingerprint

Cite this