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Authorship Identification of Source Codes

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Web and Big Data (APWeb-WAIM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10366))

Abstract

Source code authorship identification is an issue of authorship identification from documents, and it is to identify authors of source codes or programs based on source code examples of programmers. The main applications of authorship identification of source codes include software intellectual property infringement, malicious code detection and software maintenance and update. This paper proposes an approach of constructing author profiles of programmers based on a logic model of continuous word-level n-gram and discrete word-level n-gram, and a multi-level context model about operations, loops, arrays and methods. Further, we employ the technique of sequential minimal optimization for support vector machine training to identify authorship of source codes. The advantage of author profiles in this paper can discover explicit and implicit personal programming preference patterns of and between keywords, identifiers, operators, statements, methods and classes. Experimental results on programs from two open source websites demonstrate that our approach achieves a high accuracy and outperforms the baseline methods.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (NO. 61672098, NO. 61272361).

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Authors and Affiliations

  1. School of Software, Beijing Institute of Technology, Beijing, China

    Chunxia Zhang, Sen Wang & Jiayu Wu

  2. School of Computer Science, Beijing Institute of Technology, Beijing, China

    Zhendong Niu

Authors
  1. Chunxia Zhang
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  2. Sen Wang
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  3. Jiayu Wu
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  4. Zhendong Niu
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Corresponding author

Correspondence to Chunxia Zhang .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China

    Lei Chen

  2. Computer Science, Aarhus University, Aarhus N, Denmark

    Christian S. Jensen

  3. Computer Science, University of Southern California, Los Angeles, California, USA

    Cyrus Shahabi

  4. Northeastern University, Shenyang, China

    Xiaochun Yang

  5. Kent State University, Kent, Ohio, USA

    Xiang Lian

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Zhang, C., Wang, S., Wu, J., Niu, Z. (2017). Authorship Identification of Source Codes. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10366. Springer, Cham. https://doi.org/10.1007/978-3-319-63579-8_22

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  • DOI: https://doi.org/10.1007/978-3-319-63579-8_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63578-1

  • Online ISBN: 978-3-319-63579-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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