Is Call Graph Pruning Really Effective? An Empirical Re-evaluation

This artifact contains the dataset, results, and source code associated with the paper. It is divided into two archives: artifact.zip This archive includes the data used and generated in the study. Directory contents: dataset/ – Automatically generated static call graphs and their associated labels. manual_labeling/ – Edges manually sampled and labeled for evaluation. dynamic_cgs/ – Dynamic call graphs collected for each program. features/ – Structured and token-based features extracted using pre-trained CodeBERT and CodeT5 models. source_code/ – Maps each method in the programs to its corresponding source code. results/ – Contains all output files, including final results and plots used in the paper. A README file is provided within the archive for further guidance. source_code.zip This archive includes all scripts used to generate the dataset and conduct experiments. Directory contents: static_cg_generation/ – Scripts for running WALA, DOOP, and OPAL with multiple configurations to generate static call graphs. Each tool’s settings can be found under its config/ subdirectory. dataset_generation/ – Scripts for dataset construction: manual_sampling/ – Stratified sampling of call graph edges. semantic_features/ – Extraction of raw and fine-tuned semantic features. structured_features/ – Generation of structured graph features. approach/ – Machine learning experiments and evaluation pipelines described in the paper. paper/ – Scripts used to generate plots and visualizations presented in the paper. Each directory includes a README file explaining its structure and usage. This artifact enables full reproducibility of the dataset creation, feature extraction, and experimental results discussed in the paper.