Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models

By Baljinder Ghotra, Shane McIntosh, Ahmed E. Hassan. 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, 2015

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The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models (2018, methodology): “been studied in defect prediction domain [32], [33], [34], [58], [59], [60].”
An Empirical Comparison of Model Validation Techniques for Defect Prediction Models (2017, neutral): “Through a case study of 18 systems, we find that single-repetition holdout validation tends to produce estimates with 46-229 percent more bias and 53-863 percent more variance than the top-ranked model validation techniques.”
Heterogeneous Defect Prediction (2015, methodology): “05) with various learners used in defect prediction literature such as Random Forest, BayesNet, SVM, J48 Decision Tree, Simple Logistic, and Logistic Model Trees (LMT) [8, 11, 24, 25, 37, 48].”
Automated Parameter Optimization of Classification Techniques for Defect Prediction Models (2016, methodology): “[12] also show that the performance of defect prediction models can be impacted by the dataset from which they are trained.”
The Impact of Automated Parameter Optimization on Defect Prediction Models (2018, methodology): “the defect prediction models that are trained using many different variables [38].”