Artifact for ICSE-22 submission "Striking a Balance: Pruning False-Positives from Static Call Graphs"

This artifact contains 2 compressed folders for the code and data that accompanies the paper. Code The Readme in the code details the dependencies as well as the instruction on how to run the train and test-phases for the tool, and reproduce the main experimental results. The final-experiments folder contains per-benchmark results for the paper. Data This includes the following: 1) Benchmark-set (copied from NJR-1) 2) Pre-computed call-graphs 3) Pre-trained models for the cg-pruner 4) Null-pointer classification results 5) Train and test program list