---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: source
    dtype: string
  - name: file_name
    dtype: string
  - name: cwe
    dtype: string
  splits:
  - name: train
    num_bytes: 87854
    num_examples: 76
  download_size: 53832
  dataset_size: 87854
---
# Dataset Card for "static-analysis-eval"

A dataset of 76 Python programs taken from real Python open source projects (top 1000 on GitHub), 
where each program is a file that has exactly 1 vulnerability as detected by a particular static analyzer (Semgrep).

You can run the `_script_for_eval.py` to check the results.

```
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python _script_for_eval.py
```

# Leaderboard

The top models on the leaderboard are all fine-tuned using the same dataset that we released called [synth vuln fixes](https://huggingface.co/datasets/patched-codes/synth-vuln-fixes).
You can read about our experience with fine-tuning them on our [blog](https://www.patched.codes/blog/a-comparative-study-of-fine-tuning-gpt-4o-mini-gemini-flash-1-5-and-llama-3-1-8b).
You can also explore the leaderboard with this [interactive visualization](https://claude.site/artifacts/5656c16d-9751-407c-9631-a3526c259354).
![Visualization of the leaderboard](./visualization.png)

|           Model           | StaticAnalysisEval (%) |  Time (mins)  | Price (USD) |
|:-------------------------:|:----------------------:|:-------------:|:-----------:|
|   gpt-4o-mini-fine-tuned   |        77.63           |     21:0      |    0.21     |
| gemini-1.5-flash-fine-tuned |        73.68           |     18:0      |             |
| Llama-3.1-8B-Instruct-fine-tuned |        69.74           |     23:0      |             |
|       gpt-4o              |        69.74           |     24:0      |    0.12     |
|       gpt-4o-mini         |        68.42           |     20:0      |    0.07     |
| gemini-1.5-flash-latest   |        68.42           |     18:2      |    0.07     |
| Llama-3.1-405B-Instruct   |        65.78           |     40:12     |             |
| Llama-3-70B-instruct      |        65.78           |     35:2      |             |
| Llama-3-8B-instruct       |        65.78           |     31.34     |             |
| gemini-1.5-pro-latest     |        64.47           |     34:40     |             |
| gpt-4-1106-preview        |        64.47           |     27:56     |    3.04     |
|          gpt-4            |        63.16           |     26:31     |    6.84     |
| claude-3-5-sonnet-20240620|        59.21           |     23:59     |    0.70     |
|  moa-gpt-3.5-turbo-0125   |        53.95           |     49:26     |             |
| gpt-4-0125-preview        |        53.94           |     34:40     |             |
|   patched-coder-7b        |        51.31           |     45.20     |             |
|  patched-coder-34b        |        46.05           |     33:58     |    0.87     |
|    patched-mix-4x7b       |        46.05           |     60:00+    |    0.80     |
|      Mistral-Large        |        40.80           |     60:00+    |             |
|       Gemini-pro          |        39.47           |     16:09     |    0.23     |
|      Mistral-Medium       |        39.47           |     60:00+    |    0.80     |
|      Mixtral-Small        |        30.26           |     30:09     |             |
|   gpt-3.5-turbo-0125      |        28.95           |     21:50     |             |
|  claude-3-opus-20240229   |        25.00           |     60:00+    |             |
| Llama-3-8B-instruct.Q4_K_M|        21.05           |     60:00+    |             |
|      Gemma-7b-it          |        19.73           |     36:40     |             |
|   gpt-3.5-turbo-1106      |        17.11           |     13:00     |    0.23     |
| Codellama-70b-Instruct    |        10.53           |     30.32     |             |
| CodeLlama-34b-Instruct    |         7.89           |     23:16     |             |

The price is calcualted by assuming 1000 input and output tokens per call as all examples in the dataset are < 512 tokens (OpenAI cl100k_base tokenizer). 

Some models timed out during the run or had intermittent API errors. We try each example 3 times in such cases. This is why some runs are reported to be longer than 1 hr (60:00+ mins).