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--- |
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license: gpl-3.0 |
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--- |
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# Juliet_LLVM Dataset |
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[](https://huggingface.co/datasets/compAgent/Juliet_LLVM) |
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## Dataset Summary |
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**Juliet_LLVM** is a dataset of **compiled C functions from the Juliet Test Suite** (as organized in the [GitHub repository](https://github.com/arichardson/juliet-test-suite-c) |
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), 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. |
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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. |
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This dataset supports experiments described in our paper and follows the same compilation, splitting, and evaluation procedures. |
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## Key Features |
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- ✅ Based on the **Juliet Test Suite**, a standard benchmark for vulnerability detection |
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- ✅ **LLVM IR representation** of each function (field: `llvm_ir_function`) |
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- ✅ Predefined **train**, **validation**, and **test** splits |
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- ✅ **Binary vulnerability labels** (`label`) for classification |
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- ✅ Includes metadata: original file and function name |
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- ✅ Efficiently stored as **Parquet** files for fast loading and processing |
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## Dataset Structure |
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Each record contains: |
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- `dataset`: The origin source, always `"Juliet"` for this dataset |
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- `file`: Source file from the Juliet suite |
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- `fun_name`: The name of the function |
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- `llvm_ir_function`: The LLVM IR pre-processed code for the function |
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- `label`: `"1"` for vulnerable, `"0"` for non-vulnerable |
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- `split`: One of `"train"`, `"validation"`, or `"test"` |
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## Split Information |
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This dataset is split into three subsets: |
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- `train`: Used for training the models |
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- `validation`: Used for hyperparameter tuning and model selection |
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- `test`: Held out for final evaluation and benchmarking |
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> ✅ 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. |
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## Format |
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This dataset is stored in [Apache Parquet](https://parquet.apache.org/) format under the `default` configuration. It adheres to the [Croissant schema](https://mlcommons.org/croissant/) and includes metadata for fields and splits. |
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## Usage |
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You can load the dataset using the Hugging Face 🤗 `datasets` library: |
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```python |
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from datasets import load_dataset |
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# Load train split |
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train_ds = load_dataset("compAgent/Juliet_LLVM", split="train") |
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print(train_ds[0]) |
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``` |
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``` |
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{ |
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"dataset": "Juliet", |
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"file": "CWE121/s01.c", |
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"fun_name": "CWE121_bad", |
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"llvm_ir_function": "define dso_local void @CWE121_bad() { ... }", |
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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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## Related Work |
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[Juliet Test Suite](https://samate.nist.gov/SARD/test-suites) — the original source of these functions. |
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[Juliet Test Suite GitHub repository](https://github.com/arichardson/juliet-test-suite-c) - the GitHub repository we took the Juliet Test Suite dataset from. |
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## Citation |
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``` |
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@misc{juliet_llvm, |
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author = {Compote}, |
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title = {Juliet_LLVM: A Dataset of Vulnerable and Non-Vulnerable Functions from the Juliet Suite in LLVM IR}, |
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howpublished = {\url{https://huggingface.co/datasets/compAgent/Juliet_LLVM}}, |
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year = {2025} |
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} |
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``` |
