Datasets:

CCompote
/

CompRealVul_LLVM

CompRealVul_LLVM is the LLVM IR (Intermediate Representation) version of the CompRealVul_C dataset. This version is designed specifically for training and evaluating machine learning models on the task of binary vulnerability detection in a setting that closely mimics how models are used in practice — operating on the compiled representation of code rather than raw source code.

Each function in this dataset was compiled from C code to LLVM IR, enabling robust training of models on semantically rich, architecture-independent binary representations.

This dataset supports research aligned with the methodology described in our paper, where the goal is to predict vulnerabilities directly from compiled IR representations rather than from source code.

Key Features

✅ LLVM IR representation of each function (field: llvm_ir_function)
✅ Includes train, validation, and test splits (see below)
✅ Vulnerability labels (label) for supervised learning
✅ Metadata about original source (dataset, file, fun_name)

Dataset Structure

Each record contains:

dataset: Original dataset source of the function (e.g., Juliet, NVD)
file: File path of the source from which the function was extracted
fun_name: Name of the function in the source code
llvm_ir_function: LLVM IR string representing the function
label: Binary label indicating vulnerability (1 for vulnerable, 0 for non-vulnerable)
split: Dataset split (train, validation, test)

Split Information

This dataset is split into train, validation, and test sets, following the exact partitioning strategy used in the experiments described in our paper. The split ensures a fair evaluation of generalization performance by separating functions into disjoint sets with no overlap. This allows researchers to directly reproduce our results or compare against them under consistent conditions.

train: Used to fit model parameters
validation: Used for model selection and hyperparameter tuning
test: Used exclusively for final evaluation and benchmarking

Usage

You can load and explore the dataset using the 🤗 datasets library:

from datasets import load_dataset

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

## Example
```json
{
  "dataset": "CompRealVul",
  "file": "app_122.c",
  "fun_name": "app",
  "llvm_ir_function": "define dso_local i32 @app() #0 { ... }",
  "label": "1",
  "split": "train"
}

License

This dataset is released under the GPL-3.0.

Citation

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