Research graph
References from KCO: Balancing class distribution in just-in-time software defect prediction using kernel crossover oversampling. Local targets link to admitted publications; unresolved targets remain external evidence.
Companies’ Participation in OSS Development–An Empirical Study of OpenStack
10.1109/tse.2019.2946156 · 2021 · External reference
Tackling class overlap and imbalance problems in software defect prediction
2016 · External reference
Fair and balanced? Bias in bug-fix datasets
2009 · External reference
SMOTE: Synthetic Minority Over-sampling Technique
10.1613/jair.953 · 2002 · External reference
ROSE: A package for binary imbalanced learning
10.32614/rj-2014-008 · 2014 · External reference
ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In IEEE International Joint Conference on Neural Networks, 2008
2008 · External reference
MWMOTE–Majority weighted minority oversampling technique for imbalanced data set learning
10.1109/tkde.2012.232 · 2014 · External reference
A novel modified undersampling (MUS) technique for software defect prediction,”
10.1111/coin.12229 · 2019 · External reference
An empirical study to investigate oversampling methods for improving software defect prediction using imbalanced data
10.1016/j.neucom.2018.04.090 · 2019 · External reference
Progress on approaches to software defect prediction
10.1049/iet-sen.2017.0148 · 2018 · External reference
MAHAKIL: Diversity Based Oversampling Approach to Alleviate the Class Imbalance Issue in Software Defect Prediction
10.1109/tse.2017.2731766 · 2018 · External reference
An Improved MAHAKIL Oversampling Method for Imbalanced Dataset Classification
10.1109/access.2020.3047741 · 2021 · External reference
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning
2005 · External reference
A large-scale empirical study of just-in-time quality assurance
10.1109/tse.2012.70 · 2013 · External reference
TLEL: A two-layer ensemble learning approach for just-in-time defect prediction
10.1016/j.infsof.2017.03.007 · 2017 · External reference
A systematic review of unsupervised learning techniques for software defect prediction
2019 · External reference
A Systematic Survey of Just-in-Time Software Defect Prediction
10.1145/3567550 · 2023 · External reference
Machine learning in software defect prediction: A business-driven systematic mapping study
2022 · External reference
Application of Deep Learning in Software Defect Prediction: Systematic Literature Review and Meta-analysis
10.1016/j.infsof.2023.107175 · 2023 · External reference
Deep Learning for Just-in-Time Defect Prediction
10.1109/qrs.2015.14 · 2015 · External reference
DeepJIT: An end-to-end deep learning framework for just-in-time defect prediction
2019 · External reference
Effort-aware and just-in-time defect prediction with neural network
10.1371/journal.pone.0211359 · 2019 · External reference
Effort-Aware semi-Supervised just-in-Time defect prediction
10.1016/j.infsof.2020.106364 · 2020 · External reference
Within-project and cross-project just-in-time defect prediction based on denoising autoencoder and convolutional neural network
10.1049/iet-sen.2019.0278 · 2020 · External reference
A Comprehensive Investigation of the Role of Imbalanced Learning for Software Defect Prediction
2018 · External reference
Predicting the number of defects in a new software version
10.1371/journal.pone.0229131 · 2020 · External reference
Exploratory under-sampling for class-imbalance learning
2008 · External reference
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary
10.1613/jair.1.11192 · 2018 · External reference
Class Imbalance Evolution and Verification Latency in Just-in-Time Software Defect Prediction
2019 · External reference
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary
10.1613/jair.1.11192 · 2018 · External reference
Cross-Project Online Just-In-Time Software Defect Prediction
10.1109/tse.2022.3150153 · 2023 · External reference
Effectiveness of the Euclidean distance in high dimensional spaces
10.1016/j.ijleo.2015.09.093 · 2015 · External reference
How complex is your classification problem?: A survey on measuring classification complexity
10.1145/3347711 · 2019 · External reference
Simplified deep forest model based just-in-time defect prediction for android mobile apps
10.1109/tr.2021.3060937 · 2021 · External reference
Software defect prediction based on kernel PCA and weighted extreme learning machine
10.1016/j.infsof.2018.10.004 · 2019 · External reference
Combining Deep Learning and Kernel PCA for Software Defect Prediction
2022 · External reference
Examining the performance of kernel methods for software defect prediction based on support vector machine
10.1016/j.scico.2022.102916 · 2023 · External reference
Study of Influence of Dimension Reduction of High Dimensional Datasets in Classification Problem
2020 · External reference
Feature dimensionality reduction: a review
10.1007/s40747-021-00637-x · 2022 · External reference
A systematic literature review on fault prediction performance in software engineering
10.1109/tse.2011.103 · 2012 · External reference
