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--- |
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license: gpl-3.0 |
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--- |
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# CompRealVul_LLVM Dataset |
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[](https://huggingface.co/datasets/compAgent/CompRealVul_LLVM) |
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## Dataset Summary |
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**CompRealVul_LLVM** is the LLVM IR (Intermediate Representation) version of the [CompRealVul_C](https://huggingface.co/datasets/CCompote/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. |
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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. |
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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. |
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## Key Features |
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- ✅ **LLVM IR representation** of each function (field: `llvm_ir_function`) |
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- ✅ Includes **train**, **validation**, and **test** splits (see below) |
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- ✅ Vulnerability labels (`label`) for supervised learning |
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- ✅ Metadata about original source (`dataset`, `file`, `fun_name`) |
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## Dataset Structure |
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Each record contains: |
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- `dataset`: Original dataset source of the function (e.g., Juliet, NVD) |
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- `file`: File path of the source from which the function was extracted |
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- `fun_name`: Name of the function in the source code |
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- `llvm_ir_function`: LLVM IR string representing the function |
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- `label`: Binary label indicating vulnerability (`1` for vulnerable, `0` for non-vulnerable) |
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- `split`: Dataset split (`train`, `validation`, `test`) |
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## Split Information |
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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. |
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- `train`: Used to fit model parameters |
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- `validation`: Used for model selection and hyperparameter tuning |
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- `test`: Used exclusively for final evaluation and benchmarking |
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## Usage |
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You can load and explore the dataset using the 🤗 `datasets` library: |
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```python |
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from datasets import load_dataset |
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# Load a specific split |
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train_ds = load_dataset("compAgent/CompRealVul_LLVM", split="train") |
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print(train_ds[0]) |
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## Example |
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```json |
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{ |
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"dataset": "CompRealVul", |
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"file": "app_122.c", |
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"fun_name": "app", |
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"llvm_ir_function": "define dso_local i32 @app() #0 { ... }", |
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"label": "1", |
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"split": "train" |
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} |
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``` |
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## License |
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This dataset is released under the GPL-3.0. |
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## Citation |
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```cite |
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@misc{comprealvul_llvm, |
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author = {Compote}, |
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title = {CompRealVul_LLVM: A Dataset of Vulnerable and Non-Vulnerable Functions in LLVM IR}, |
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howpublished = {\url{https://huggingface.co/datasets/compAgent/CompRealVul_LLVM}}, |
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year = {2025} |
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} |
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``` |
