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.