---
pretty_name: CheckerBench
language:
- en
size_categories:
- n<1K
tags:
- code
- static-analysis
- vulnerability-detection
- codeql
- clang-static-analyzer
- benchmark
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test.parquet
---

# CheckerBench Dataset

**300 static-analysis tasks with complete runtime assets: 159 CSA tasks and 141 CodeQL tasks.**

| Backend / language | Tasks |
|---|---:|
| CSA (C/C++) | 159 |
| CodeQL Go | 30 |
| CodeQL Java | 30 |
| CodeQL JavaScript | 30 |
| CodeQL Python | 51 |
| **Total** | **300** |

`dataset/tasks.jsonl` is the authoritative portable roster. `tasks/` contains browsable task inputs. The `assets/` directory provides complete task bundles, reference implementations, source snapshots, CodeQL databases, CSA build assets, refinement environments, and a frozen Docker image.

## Load the task table

```python
from datasets import load_dataset

tasks = load_dataset("Benchanything/checkerbench", split="test")
assert len(tasks) == 300
```

The single `test` split contains all 300 benchmark tasks; no training or validation split is provided. Each row includes the task ID, static analyzer (`static_analyzer`: `csa` or `codeql`), language, CVE and source-project identifiers, task prompt, patch, before/after context, and task configuration as JSON text. `checker_template` contains the CSA starter implementation and is null for CodeQL tasks. The original browsable inputs are also available under `tasks/`. `task_path` identifies the task inside its restored bundle, while `environment_id` identifies its runtime environment.

## Download and restore

Requirements: Linux x86-64, Docker, Python 3.10+, and at least 100 GB of available disk space. Additional task workspaces require additional storage.

Download the complete distribution, including the datasets and frozen runtime image (approximately 13.3 GB of compressed assets):

```bash
pip install -U huggingface_hub
hf download Benchanything/checkerbench --repo-type dataset --local-dir checkerbench
cd checkerbench
python3 scripts/restore.py --assets assets --data runtime_data --load-image
```

For a private repository, authenticate with `hf auth login` using an account with access before downloading. Public downloads do not require authentication.

To download only dataset archives using a local copy of this repository's scripts:

```bash
python3 scripts/download.py --kind dataset --output assets
```

The downloader verifies every part against its SHA-256 checksum, and the restore script also verifies the complete compressed stream. Split archives are extracted or loaded directly without writing an extra combined copy. Verified downloads and completed restores can be reused.

## Create a task workspace

The `checkerbench-runtime:review-v1` image targets `linux/amd64` and includes LLVM/Clang 18.1.8, CodeQL 2.26.4, language toolchains, and Python dependencies. Model-service credentials are supplied separately by the operator.

Start a container with stable paths:

```bash
mkdir -p work
docker run --rm -it --network none --entrypoint /bin/bash \
  -v "$PWD:/release:ro" -v "$PWD/runtime_data:/data:ro" \
  -v "$PWD/work:/work" checkerbench-runtime:review-v1
```

Prepare a CSA task:

```bash
python /release/scripts/prepare.py csa pair_134943 \
  --data-root /data --output /work/pair_134943
cd /work/pair_134943
# Write checker.cpp, then compile and scan.
bash compile.sh
CHECKER_NAME=YOUR_CHECKER_NAME bash scan/scan_before.sh
CHECKER_NAME=YOUR_CHECKER_NAME bash scan/scan_after.sh
```

Prepare a CodeQL task:

```bash
python /release/scripts/prepare.py codeql morefixes-32ed35a8bd8aba7b47ad \
  --language go --data-root /data --output /work/go-example
cd /work/go-example/tasks/morefixes-32ed35a8bd8aba7b47ad
# Edit query/vulnerability.ql, then compile and scan.
bash scripts/compile_query.sh
bash scripts/run_before.sh
bash scripts/run_after.sh
```

Only tasks in the fixed 300-task roster are accepted. Maintainers can add `--reference` to reproduce a bundled reference implementation. Keep the same container paths after preparing a workspace. CSA uses shared Git objects from the read-only dataset; CodeQL databases are copied into the task workspace.

## Dataset contents

- **CSA:** 159 tasks with source snapshots, captured build assets, reference implementations, and refinement environments. Shared runtime assets preserve restoration dependencies.
- **CodeQL:** four task bundles containing 30 Go, 30 Java, 30 JavaScript, and 51 Python tasks, with their captured analysis databases.
- `scripts/prepare.py` restores before/after scanning workspaces. The CSA bundle also includes `scripts/rehydrate_benchmark_env.py` for full refinement worktrees. Historical refinement assets contain absolute paths and symbolic links; see `docs/environment.md` for the restoration boundary.
- References and evaluator assets are maintainer material. During evaluation, expose only a prepared single-task workspace and its required runtime assets to the evaluated agent.

## Integrity and validation

`release-manifest.json` records archive and part sizes, SHA-256 checksums, and the image fingerprint. `environment/versions.json` records the toolchain versions; the exported image is the direct restoration entry point. `reports/validation.json` records the checks actually performed and their limits.

Third-party source and tools retain their own license files. This repository does not assign a new blanket license to all bundled third-party content.


## Paper

[CheckerBench: Can Long-Horizon Agents Synthesize Static-Analysis Checkers?](https://arxiv.org/abs/2610.07557)