A cross‐project defect prediction method based on multi‐adaptation and nuclear norm
Abstract Cross‐project defect prediction (CPDP) is an important research direction in software defect prediction. Traditional CPDP methods based on hand‐crafted features ignore the semantic information in the source code. Existing CPDP methods based on the deep learning model may not fully consider...
Saved in:
Main Authors: | Qingan Huang, Le Ma, Siyu Jiang, Guobin Wu, Hengjie Song, Libiao Jiang, Chunyun Zheng |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2022-04-01
|
Series: | IET Software |
Subjects: | |
Online Access: | https://doi.org/10.1049/sfw2.12053 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Correlation feature and instance weights transfer learning for cross project software defect prediction
by: Quanyi Zou, et al.
Published: (2021-02-01) -
Leveraging an Enhanced CodeBERT-Based Model for Multiclass Software Defect Prediction via Defect Classification
by: Rida Ghafoor Hussain, et al.
Published: (2025-01-01) -
Revisiting ‘revisiting supervised methods for effort‐aware cross‐project defect prediction’
by: Fuyang Li, et al.
Published: (2023-08-01) -
Convolutional Neural Networks for Software Defect Categorization: An Empirical Validation
by: Ruchika Malhotra, et al.
Published: (2025-01-01) -
Improving source code suggestion with code embedding and enhanced convolutional long short‐term memory
by: Yasir Hussain, et al.
Published: (2021-06-01)