README.md

metadata

license: cc-by-4.0
pretty_name: BCSD Dataset (Binary Code Similarity Detection)
language:
  - en
task_categories:
  - feature-extraction
tags:
  - binary-code-similarity
  - bcsd
  - reverse-engineering
  - disassembly
  - x86-64
size_categories:
  - 100K<n<1M

BCSD Dataset

A dataset for Binary Code Similarity Detection (BCSD). It contains C/C++ programs compiled with two compilers (GCC, Clang) at five optimization levels (O0, O1, O2, O3, Os) on x86-64, together with their disassembly and precomputed function embeddings.

Contents

File	Size	Description
sources.tar.zst	22 MB	Original source files (C, C++, Go, Rust)
binaries.tar.zst	1.85 GB	Compiled ELF binaries (114,532 files)
disasm.tar.zst	47 MB	Linear disassembly (angr), one JSON per binary
disasm_jtrans.tar.zst	99 MB	Basic-block disassembly for jTrans models
embeddings.tar.zst	538 MB	Per-function embeddings (.npy) for each approach

Statistics: 11,639 source programs over 5,212 problems (AtCoder, LeetCode, Rosetta Code).

Path layout

The binaries, disasm, disasm_jtrans and embeddings archives use the same layout:

<compiler>/<arch>/<optim>/<dataset>/<problem>/<Lang>__impl_NN
example: clang/x86_64/Os/rosetta_code/binary_digits/Cpp__impl_01

Sources use sources/<dataset>/<problem>/<Lang>/impl_NN.ext.

Download

pip install -U "huggingface_hub[cli]"

# one file
hf download <user>/bcsd-dataset disasm.tar.zst --repo-type dataset --local-dir .

# everything
hf download <user>/bcsd-dataset --repo-type dataset --local-dir ./bcsd-dataset

Decompress

The archives are compressed with zstd. Install zstd (apt install zstd or brew install zstd), then extract:

tar --use-compress-program=unzstd -xf disasm.tar.zst
# or, with a recent tar:
tar --zstd -xf disasm.tar.zst

Usage

Read a disassembly file (one JSON per binary):

import json
d = json.load(open("disasm/clang/x86_64/Os/rosetta_code/binary_digits/Cpp__impl_01.json"))
for fn in d["functions"]:
    print(fn["name"], fn["nb_instructions"])
    for mnemonic, operands in fn["instructions"]:
        print(mnemonic, operands)

Load a function embedding (shape is (n_functions, dim)):

import numpy as np
v = np.load("embeddings/palmtree/clang/x86_64/Os/rosetta_code/binary_digits/Cpp__impl_01.npy")
print(v.shape)  # (1, 128)

Embedding dimensions: baseline = 16, palmtree / refuse = 128, jtrans = 768. The file embeddings/index.json maps each source_id::function to its embedding paths.

Notes

Only C and C++ are compiled in this release; Go and Rust appear in the sources only. The dataset is x86-64 only. Source programs come from AtCoder, LeetCode and Rosetta Code; please respect their terms when reusing.