SoCodeCNN: Program Source Code for Visual CNN Classification Using Computer Vision Methodology

2019/01/01 by Somdip Dey, Amit Kumar Singh, Dilip K. Prasad +1 · 2 citations
Engineering · Biochemistry, Genetics and Molecular Biology · #Image Processing Techniques and Applications #CCD and CMOS Imaging Sensors #Cell Image Analysis Techniques

paper · pdf · doi:10.1109/access.2019.2949483

openalex publication_date 2019/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/22

Abstract

Automated feature extraction from program source-code such that proper computing resources could be allocated to the program is very difficult given the current state of technology. Therefore, conventional methods call for skilled human intervention in order to achieve the task of feature extraction from programs. This research is the first to propose a novel human-inspired approach to automatically convert program source-codes to visual images. The images could be then utilized for automated classification by visual convolutional neural network (CNN) based algorithm. Experimental results show high prediction accuracy in classifying the types of program in a completely automated manner using this approach.

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• ImageNet Large Scale Visual Recognition Challenge
• ImageNet classification with deep convolutional neural networks
• A Survey on Transfer Learning

Cited by

• User Interaction Aware Reinforcement Learning for Power and Thermal Efficiency of CPU-GPU Mobile MPSoCs
• National Centre for Nuclear Robotics [wikipedia]