The purpose of this experiment is to find unlabelled security bugs among thousands of bug reports via active learning and ranking. This experiment simulates the review process under the specified SBR(Security Bugs Report) recall rate.
encoder/: Different method to transform words into vectorsmodel/: Training different models and predict the probability of SBRexperiment/: Experiments to runRQ*.py: Runner
All configurations are in BaseExperiment class, and we use ExperimentFactory to build its instance from a list of configurations dictionary.
pip install numpy pandas sklearn alive_progress
python3 RQ*.py