|
--- |
|
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 |
|
--- |
|
# Static Analysis Eval Benchmark |
|
|
|
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 |
|
``` |
|
|
|
For all supported options, run with `--help`: |
|
|
|
``` |
|
usage: _script_for_eval.py [-h] [--model MODEL] [--cache] [--n_shot N_SHOT] [--use_similarity] |
|
|
|
Run Static Analysis Evaluation |
|
|
|
options: |
|
-h, --help show this help message and exit |
|
--model MODEL OpenAI model to use |
|
--cache Enable caching of results |
|
--n_shot N_SHOT Number of examples to use for few-shot learning |
|
--use_similarity Use similarity for fetching dataset examples |
|
``` |
|
|
|
We need to use the logged in version of Semgrep to get access to more rules for vulnerability detection. So, make sure you login before running the eval script. |
|
|
|
``` |
|
% semgrep login |
|
API token already exists in /Users/user/.semgrep/settings.yml. To login with a different token logout use `semgrep logout` |
|
``` |
|
|
|
After the run, the script will also create a log file which captures the stats for the run and the files that were fixed. |
|
You can see an example [here](https://huggingface.co/datasets/patched-codes/static-analysis-eval/blob/main/gpt-4o-mini_semgrep_1.85.0_20240818_215254.log). |
|
|
|
# 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). |
|
 |
|
|
|
| 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). |
|
|
|
If you want to add your model to the leaderboard, you can send in a PR to this repo with the log file from the evaluation run. |
