A Suitable AST Node Granularity and Multi-Kernel Transfer Convolutional Neural Network for Cross-Project Defect Prediction

A Suitable AST Node Granularity and Multi-Kernel Transfer Convolutional Neural Network for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) is a feasible way to perform software defect prediction (SDP) when lacking historical data. Recent CPDP approaches have employed deep learning techniques to better exploit the information from the program's abstract syntax trees (ASTs). However, the granul...

Full description

Bibliographic Details
Main Authors: Jiehan Deng, Lu Lu, Shaojian Qiu, Yangpeng Ou
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Abstract syntax tree
cross-project defect prediction
maximum mean discrepancy
multi-kernel
transfer learning
Online Access:https://ieeexplore.ieee.org/document/9057380/

Internet

https://ieeexplore.ieee.org/document/9057380/

Similar Items