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Related Experiment Video

Updated: Feb 19, 2026

Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder
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Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder

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Authorship attribution of source code by using back propagation neural network based on particle swarm optimization.

Xinyu Yang1, Guoai Xu1, Qi Li1

  • 1National Engineering Lab for Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing, China.

Plos One
|November 3, 2017
PubMed
Summary

This study introduces a novel method for Java source code authorship attribution using a hybrid particle swarm optimization and back propagation neural network. The approach achieves 91.06% accuracy, outperforming existing techniques for programmer identification.

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Last Updated: Feb 19, 2026

Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder
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Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder

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Area of Science:

  • Computer Science
  • Software Engineering
  • Artificial Intelligence

Background:

  • Authorship attribution identifies the original author of a text or code.
  • Source code authorship attribution is crucial for plagiarism detection, security, and dispute resolution.
  • Existing methods for Java source code attribution require improvement in accuracy and efficiency.

Purpose of the Study:

  • To propose a novel, highly accurate method for Java source code authorship attribution.
  • To enhance the identification of programmers through advanced machine learning techniques.
  • To evaluate the proposed method's performance against established approaches.

Main Methods:

  • A hybrid approach combining Particle Swarm Optimization (PSO) and Back Propagation (BP) neural networks was developed.
  • Feature extraction involved 19 metrics, including lexical, layout, structural, and syntax features of Java source code.
  • Supervised learning was employed, with PSO optimizing the weights of the BP neural network.

Main Results:

  • The proposed method achieved a high accuracy rate of 91.06% in Java source code authorship attribution.
  • Evaluation was conducted on a dataset comprising 3,022 Java files from 40 distinct authors.
  • The method demonstrated superior performance compared to previous works on Java source code authorship attribution.

Conclusions:

  • The PSO-BP hybrid neural network offers a highly effective solution for Java source code authorship attribution.
  • This method provides a significant advancement in identifying programmers with high precision.
  • The approach is effective and maintains acceptable computational overhead.