Juliet_LLVM Dataset

Dataset Summary

Juliet_LLVM is a dataset of compiled C functions from the Juliet Test Suite (as organized in the GitHub repository ), translated into LLVM Intermediate Representation (IR) after pre-process phase. It is designed for training and evaluating machine learning models on the task of binary vulnerability detection. Each function is labeled as either vulnerable or non-vulnerable and is presented in an architecture-agnostic, semantically rich format.

This LLVM version allows models to be trained and tested on realistic, compiler-transformed code — reflecting how such models would be deployed in real-world binary analysis scenarios.

This dataset supports experiments described in our paper and follows the same compilation, splitting, and evaluation procedures.

Key Features

• ✅ Based on the Juliet Test Suite, a standard benchmark for vulnerability detection
• ✅ LLVM IR representation of each function (field: llvm_ir_function)
• ✅ Predefined train, validation, and test splits
• ✅ Binary vulnerability labels (label) for classification
• ✅ Includes metadata: original file and function name
• ✅ Efficiently stored as Parquet files for fast loading and processing

Dataset Structure

Each record contains:

• dataset: The origin source, always "Juliet" for this dataset
• file: Source file from the Juliet suite
• fun_name: The name of the function
• llvm_ir_function: The LLVM IR pre-processed code for the function
• label: "1" for vulnerable, "0" for non-vulnerable
• split: One of "train", "validation", or "test"

Split Information

This dataset is split into three subsets:

• train: Used for training the models
• validation: Used for hyperparameter tuning and model selection
• test: Held out for final evaluation and benchmarking

✅ These splits match exactly the partitioning used in our paper experiments, allowing reproducibility and direct comparison with our reported results. Each split is disjoint and ensures no function-level overlap between sets.

Format

This dataset is stored in Apache Parquet format under the default configuration. It adheres to the Croissant schema and includes metadata for fields and splits.

Usage

You can load the dataset using the Hugging Face 🤗 datasets library:

from datasets import load_dataset

# Load train split
train_ds = load_dataset("compAgent/Juliet_LLVM", split="train")
print(train_ds[0])

{
  "dataset": "Juliet",
  "file": "CWE121/s01.c",
  "fun_name": "CWE121_bad",
  "llvm_ir_function": "define dso_local void @CWE121_bad() { ... }",
  "label": "1",
  "split": "train"
}

License

This dataset is released under the GPL-3.0.

Related Work

Juliet Test Suite — the original source of these functions.

Juliet Test Suite GitHub repository - the GitHub repository we took the Juliet Test Suite dataset from.

Citation

@misc{juliet_llvm,
  author       = {Compote},
  title        = {Juliet_LLVM: A Dataset of Vulnerable and Non-Vulnerable Functions from the Juliet Suite in LLVM IR},
  howpublished = {\url{https://huggingface.co/datasets/compAgent/Juliet_LLVM}},
  year         = {2025}
}