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The Impact of Using Regression Models to Build Defect Classifiers
We find that: i) Random forest based classifiers outperform other classifiers (best AUC) for both classifier building approaches; ii) In contrast to common practice, building a defect classifier using discretized defect counts (i. e., discretized classifiers) does not always lead to better performance.
Impact of Discretization Noise of the Dependent variable on Machine Learning Classifiers in Software Engineering
Researchers usually discretize a continuous dependent variable into two target classes by introducing an artificial discretization threshold (e. g., median).
The impact of feature importance methods on the interpretation of defect classifiers
We further observe that the commonly used defect datasets are rife with feature interactions and these feature interactions impact the computed feature importance ranks of the CS methods (not the CA methods).
Towards effective AI-powered agile project management
The rise of Artificial intelligence (AI) has the potential to significantly transform the practice of project management.
