Labradorlabs/bsca-sca-benchmark-v1

BSCA SCA Benchmark v1

Benchmark dataset for evaluating Binary Software Composition Analysis (SCA) — identifying which open-source libraries are present in a stripped binary.

Dataset Description

10 real-world stripped ELF binaries with ground-truth component labels. Used to evaluate embedding-based SCA systems that match binary functions against open-source code.

Attribution: The binaries and ground-truth labels in this dataset are derived and refined from the artifact of the paper: "Are We There Yet? Filling the Gap Between Binary Similarity Analysis and Binary Software Composition Analysis" — Artifact: https://sites.google.com/view/bsa2bsca/home/artifact

Fields

Field	Type	Description
binary_name	string	Filename of the binary (e.g. db_bench.bin)
binary	bytes	Raw binary content (stripped ELF / shared object)
components	list[dict]	Ground-truth open-source components included in the binary
components[].source	string	Git repository URL of the component
components[].tag	list[string]	Version tags (empty list = version unknown)

Binaries

Binary	Size	Components
controlblock.bin	683 KB	8
db_bench.bin	5.5 MB	15
dosbox_core_libretro.so	7.1 MB	19
example.bin	773 KB	6
hyriseSystemTest.bin	4.0 MB	10
kvrocks.bin	11.9 MB	9
pagespeed_automatic_test.bin	29.1 MB	34
prometheus_test.bin	2.3 MB	5
replay-sorcery.bin	5.5 MB	11
turbobench.bin	4.4 MB	30

• Architecture: x86-64
• Compiler: GCC (various versions)
• Optimization: -O2
• Strip: Fully stripped (no symbol names)

Usage

from datasets import load_dataset

ds = load_dataset("Labradorlabs/bsca-sca-benchmark-v1", split="train")

for row in ds:
    print(row["binary_name"])
    print([c["source"] for c in row["components"]])
    # row["binary"] contains raw binary bytes

Evaluation

# Write binary to disk for analysis
import tempfile, os
from datasets import load_dataset

ds = load_dataset("Labradorlabs/bsca-sca-benchmark-v1", split="train")
row = ds[0]  # db_bench

with tempfile.NamedTemporaryFile(suffix=".bin", delete=False) as f:
    f.write(row["binary"])
    bin_path = f.name

# Run your SCA tool on bin_path and compare against row["components"]

Evaluation Metric

Component-level Precision / Recall / F1 on identifying included open-source libraries:

• Predicted: set of repository URLs your SCA tool identifies
• Ground truth: components[].source URLs

Precision = |predicted ∩ ground_truth| / |predicted|
Recall    = |predicted ∩ ground_truth| / |ground_truth|
F1        = 2 * P * R / (P + R)

Related Models

• Labradorlabs/bsca-bge-micro-v2-contrastive-v1
• Labradorlabs/bsca-all-minilm-contrastive-v1

Citation

If you use this dataset, please also cite the original paper from which the binaries and ground truth were derived:

@inproceedings{bsa2bsca,
  title={Are We There Yet? Filling the Gap Between Binary Similarity Analysis and Binary Software Composition Analysis},
  url={https://sites.google.com/view/bsa2bsca/home/artifact}
}

@misc{bsca-benchmark-2026,
  title={BSCA SCA Benchmark v1},
  author={Labradorlabs},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/Labradorlabs/bsca-sca-benchmark-v1}
}