ICodeNet -- A Hierarchical Neural Network Approach for Source Code Author Identification

TL;DR

This paper introduces ICodeNet, a hierarchical neural network that processes source code images for author identification, demonstrating improved accuracy over simpler models and highlighting the effectiveness of image-based approaches.

Contribution

ICodeNet is a novel hierarchical neural network that combines image processing with neural classifiers for source code author identification, outperforming traditional text-based methods.

Findings

ICodeNet achieves higher accuracy than text-based models.

Image-based hierarchical neural networks are effective for source code author identification.

Model variations show the robustness of the approach.

Abstract

With the open-source revolution, source codes are now more easily accessible than ever. This has, however, made it easier for malicious users and institutions to copy the code without giving regards to the license, or credit to the original author. Therefore, source code author identification is a critical task with paramount importance. In this paper, we propose ICodeNet - a hierarchical neural network that can be used for source code file-level tasks. The ICodeNet processes source code in image format and is employed for the task of per file author identification. The ICodeNet consists of an ImageNet trained VGG encoder followed by a shallow neural network. The shallow network is based either on CNN or LSTM. Different variations of models are evaluated on a source code author classification dataset. We have also compared our image-based hierarchical neural network model with simple image-based CNN architecture and text-based CNN and LSTM models to highlight its novelty and efficiency.