Artifact for ICSE-22 submission "Striking a Balance: Pruning False-Positives from Static Call Graphs"
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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md5:ea8689582030263833f659ddba536cef
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6.5 MB | Download |
md5:1d1bdebfe6697fe82a8388bfca2d8e66
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245.5 MB | Download |
md5:b35b584eec1f026ca2a59767d5e15e05
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820.4 kB | Preview Download |
md5:d1ba9f3219bf57806800a673746fd394
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569 Bytes | Preview Download |
md5:5539a56f2a2bad23e2ba85c6c8b16abd
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1.6 kB | Download |
md5:a6fc31e0157d8a75cf3f1f3779bbab75
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9.5 kB | Preview Download |
md5:23f17847bd1b07f790470c895e7a517b
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201 Bytes | Preview Download |
md5:fa0c7132ec09ef41ed25122988321fb4
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3.6 MB | Download |
md5:e8bd2d6e71b9a66cb04da8664dacef5d
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424 Bytes | Preview Download |
md5:a39e87ed2c6b30e2f7f1eafc55795102
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10.9 GB | Download |