Online Appendix for Towards Project-Aware Actionability Detection for Coding Rule Violations

Actionability Detection – Results Contents active_learning_cycle_results/Contains the raw experimental outputs for all projects. The folder includes JSON files and PNG plots reporting per-fold and aggregated metrics such as F1-score, precision, and recall. active_learning_cycle_table_results/Provides readable summaries of the active-learning experiments. The table compiles F1-score trajectories and final performance values across all evaluated projects. active_learning_scripts/Includes the Python scripts used to run the active learning framework, perform model training, execute iterative querying, and generate all reported metrics and visualizations. svd_variance_test/Contains the script and output files related to the TruncatedSVD variance analysis, including JSON results and variance-curve visualizations used to determine the final dimensionality setting.