# TCNN

**Repository Path**: baptism/TCNN

## Basic Information

- **Project Name**: TCNN
- **Description**: Transfer Convolutional Neural Network for Cross-Project Defect Prediction
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-02-26
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# TCNN

Transfer Convolutional Neural Network for Cross-Project Defect Prediction.

TCNN aims to mine the transferable semantic (deep-learning (DL)-generated) features for CPDP tasks. Specifically, our approach first parses the source file into integer vectors as the network inputs. Next, to obtain the TCNN model, a matching layer is added into convolutional neural network where the hidden representations of the source and target project-specific data are embedded into a reproducing kernel Hilbert space for distribution matching. By simultaneously minimizing classification error and distribution divergence between projects, the constructed TCNN could extract the transferable DL-generated features. Finally, without losing the information contained in handcrafted features, we combine them with transferable DL-generated features to form the joint features for CPDP performing.



Build running environment
=================
1. Anaconda python 3.6 version (https://www.anaconda.com)

2. Pytorch 0.4.1 (https://pytorch.org)

Demo
=================
After environment building, please run following file:

1. runTra.py is used to perform traditional methods.

2. runCNN.m is used for CNN/DPDBN performing.

3. runDBN.m is used for DBN/DPDBN performing.

4. runTCNN.m is used for TCNN/DPTCNN performing.

Contacts
=================
If any issues, please feel free to contact the author.

**Author Name**: Kevin Qiu

**Author Email**: qiushaojian@outlook.com