Training data selection for imbalanced cross-project defect prediction

2021 ◽  
Vol 94 ◽  
pp. 107370
Author(s):  
Shang Zheng ◽  
Jinjing Gai ◽  
Hualong Yu ◽  
Haitao Zou ◽  
Shang Gao
Keyword(s):  
Training Data ◽  
Data Selection ◽  
Defect Prediction ◽  
Selection For ◽  
Training Data Selection ◽  
Cross Project

scholarly journals An Empirical Study of Training Data Selection Methods for Ranking-Oriented Cross-Project Defect Prediction

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7535
Author(s):  
Haoyu Luo ◽  
Heng Dai ◽  
Weiqiang Peng ◽  
Wenhua Hu ◽  
Fuyang Li
Keyword(s):  
Training Data ◽  
Data Selection ◽  
Defect Prediction ◽  
Selection Methods ◽  
Industrial Project ◽  
Software Modules ◽  
Industrial Projects ◽  
Project Data ◽  
Training Data Selection ◽  
Cross Project

Ranking-oriented cross-project defect prediction (ROCPDP), which ranks software modules of a new target industrial project based on the predicted defect number or density, has been suggested in the literature. A major concern of ROCPDP is the distribution difference between the source project (aka. within-project) data and target project (aka. cross-project) data, which evidently degrades prediction performance. To investigate the impacts of training data selection methods on the performances of ROCPDP models, we examined the practical effects of nine training data selection methods, including a global filter, which does not filter out any cross-project data. Additionally, the prediction performances of ROCPDP models trained on the filtered cross-project data using the training data selection methods were compared with those of ranking-oriented within-project defect prediction (ROWPDP) models trained on sufficient and limited within-project data. Eleven available defect datasets from the industrial projects were considered and evaluated using two ranking performance measures, i.e., FPA and Norm(Popt). The results showed no statistically significant differences among these nine training data selection methods in terms of FPA and Norm(Popt). The performances of ROCPDP models trained on filtered cross-project data were not comparable with those of ROWPDP models trained on sufficient historical within-project data. However, ROCPDP models trained on filtered cross-project data achieved better performance values than ROWPDP models trained on limited historical within-project data. Therefore, we recommended that software quality teams exploit other project datasets to perform ROCPDP when there is no or limited within-project data.

Data-driven method for training data selection for deep learning

2021 ◽  
Author(s):  
C. Lacombe ◽  
I. Hammoud ◽  
J. Messud ◽  
H. Peng ◽  
T. Lesieur ◽  
...  
Keyword(s):  
Deep Learning ◽  
Training Data ◽  
Data Selection ◽  
Data Driven ◽  
Selection For ◽  
Training Data Selection

Training data selection for acoustic modeling via submodular optimization of joint kullback-leibler divergence

2015 ◽  
Author(s):  
Taichi Asami ◽  
Ryo Masumura ◽  
Hirokazu Masataki ◽  
Manabu Okamoto ◽  
Sumitaka Sakauchi
Keyword(s):  
Training Data ◽  
Acoustic Modeling ◽  
Data Selection ◽  
Submodular Optimization ◽  
Leibler Divergence ◽  
Selection For ◽  
Training Data Selection
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