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Published August 10, 2021 | Version v5
Software Open

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

Creators

  • 1. Anonymous

Description

This artifact contains 3 compressed folders for the code, data and results that accompany the paper. The VirtualBox VM image already contains the code, data, benchmarks and has all the dependencies resolved. Please read the README.md file for information about the artifact.

Code

The code to reproduce the experimental results in the paper. 

Data

This includes the following:

1) Pre-computed call-graphs

2) Pre-trained models for the cg-pruner

3) Train and test program lists

The benchmark set used is the publicly available NJR-1 dataset (https://zenodo.org/record/4839913) 

Results

This contains detailed (i.e. per-benchmark) results for the paper, including the data points used to plot the graphs.

Files

icse2022-paper1207.pdf

Files (11.2 GB)

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