SVA-ICL: Improving LLM-based Software Vulnerability Assessment via In-Context Learning and Information Fusion

This is the source code to the paper "SVA-ICL: Improving LLM-based Software Vulnerability Assessment via In-Context Learning and Information Fusion". Please refer to the paper for the experimental details.

Approach

About dataset.

1. The dataset folder contains all the data used in the experiments for RQ1-RQ5.
2. The dataset2 and dataset3 folders store the additional two random samples used in the discussion section.
3. Due to the large size of the datasets, we have stored them in Google Drive: Google Drive Link.

About the experimental results in the paper:

1. The results for RQ1 and RQ2 are stored in the results3 and results2 folders, respectively.
2. The results for RQ3 and RQ4 are stored in the results_RQ3 and results_RQ4 folders, respectively.
3. The results for RQ5 are stored in the results folder.
4. The experimental results for the discussion section are stored in the results_gpt35, results_gpt4o, results_dataset2, and results_dataset3 folders.

About the models:

We use the bert_whitening trained models, which are stored in the model, model_dataset2, and model_dataset3 folders.

For reproducing the experiments:

1. Use the provided Jupyter files for data preprocessing.
2. Run bert_whitening.py. After running, we get the semantic vector library of the training set, kernel, and bias.
3. Run ccgir.py to get the most similar code fragments for the test set.
4. Run search_info_form_code.ipynb to get all the data required for the prompt template.
5. Run deepseek.ipynb to call the LLM and complete the vulnerability assessment task.