Authorship Identification of Source Code Segments Written by Multiple Authors Using Stacking Ensemble Method

TL;DR

This paper introduces a stacking ensemble classifier combining neural networks, random forests, and SVMs to identify multiple authors of source code segments, significantly improving accuracy over existing single-author methods.

Contribution

It presents a novel ensemble approach capable of accurately identifying multiple authors in source code, addressing a gap in existing single-author focused techniques.

Findings

Enhanced accuracy in multi-author source code attribution

Outperforms existing single-author identification methods

Effective in complex authorship scenarios

Abstract

Source code segment authorship identification is the task of identifying the author of a source code segment through supervised learning. It has vast importance in plagiarism detection, digital forensics, and several other law enforcement issues. However, when a source code segment is written by multiple authors, typical author identification methods no longer work. Here, an author identification technique, capable of predicting the authorship of source code segments, even in the case of multiple authors, has been proposed which uses a stacking ensemble classifier. This proposed technique is built upon several deep neural networks, random forests and support vector machine classifiers. It has been shown that for identifying the author group, a single classification technique is no longer sufficient and using a deep neural network-based stacking ensemble method can enhance the accuracy significantly. The performance of the proposed technique has been compared with some existing methods which only deal with the source code segments written precisely by a single author. Despite the harder task of authorship identification for source code segments written by multiple authors, our proposed technique has achieved promising results evidenced by the identification accuracy, compared to the related works which only deal with code segments written by a single author.