Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- codellama/CodeLlama-7b-Instruct-hf
+pipeline_tag: text-classification
+library_name: transformers
+tags:
+- code
+---
+# Model Card for vuteco-cl-e2e
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+`vuteco-cl-e2e` is a fine-tuned [Codellama 7B Instruct](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) that classifies pairs of JUnit test methods and vulnerability descriptions (from CVE) into two classes:
+- `Related` if it the method is testing the vulnerability described.
+- `NotRelated` if it the method is not testing the vulnerability described.
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+VuTeCo is a framework for finding vulnerability-witnessing test cases in Java repositories (Finding) and match them with the right known vulnerability (Matching).
+More info in its [GitHub repository](https://github.com/tuhh-softsec/vuteco).
+
+This model (`vuteco-cl-e2e`) is a fine-tuned [Codellama 7B Instruct](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) with a simple classification prompt.
+
+This model is used in VuTeCo for the "Matching" task, which can classify a pair of (1) JUnit test method and (2) an English description of a vulnerability (e.g., the one from CVE) into two classes:
+- `Related` if it the method is testing the vulnerability described.
+- `NotRelated` if it the method is not testing the vulnerability described.
+
+The model input is (1) the raw text of a JUnit test method and (2) the raw text of a vulnerability description, both with no preprocessing.
+
+- **Developed by:** Hamburg University of Technology
+- **Funded by:** [Sec4AI4Sec](https://www.sec4ai4sec-project.eu/) (Horizon EU)
+- **Shared by:**: Hugging Face
+- **Model type:** Text Classification
+- **Language(s) (NLP):** en
+- **License:** Apache-2.0
+- **Finetuned from model:** [Codellama 7B Instruct](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf)
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [VuTeCo's GitHub repository](https://github.com/tuhh-softsec/vuteco)
+- **Paper:** [MSR'26 paper](https://arxiv.org/abs/2502.03365)
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+The model can be used right away to classify specific types of vulnerability-witnessing tests, e.g., distinguishing the exact vulnerability types that is tested.
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+The model can be further fine-tuned to classify specific types of vulnerability-witnessing tests, e.g., distinguishing the exact vulnerability types that is tested.
+
+It could also be fine-tuned for other testing frameworks (beyond JUnit) and programming languages (Python).
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+N/A
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+The model predictions may be inaccurate (misclassified test methods).
+In particular, the reported performance show the model has limited recall, so it often says `NotRelated`.
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Manually validate the predictions made by the model.
+
+## How to Get Started with the Model
+
+Please, refer to [VuTeCo's GitHub repository](https://github.com/tuhh-softsec/vuteco) for loading and using the model in the correct way.
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+This model was fine-tuned on Java repositories and vulnerabilities from [Vul4J](https://github.com/tuhh-softsec/vul4j).
+Please refer to [VuTeCo's GitHub repository](https://github.com/tuhh-softsec/vuteco) for loading the dataset in the correct way.
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+Please refer to [VuTeCo's GitHub repository](https://github.com/tuhh-softsec/vuteco) for customizing the model training.
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+Please refer to [VuTeCo's GitHub repository](https://github.com/tuhh-softsec/vuteco) for customizing the model evaluation.
+
+### Results
+
+Please, refer to the [MSR'26 paper](https://arxiv.org/abs/2502.03365) for an overview of the main evaluation results.
+The complete raw results can be found in the paper's online appendix on [Zenodo](https://doi.org/10.5281/zenodo.18258566).
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+N/A
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+If you use this model, please cite the [MSR'26 paper](https://arxiv.org/abs/2502.03365) (the publisher's reference will be available soon):
+
+**BibTeX:**
+
+```
+@misc{iannone2026matchheavenaidrivenmatching,
+    title={A Match Made in Heaven? AI-driven Matching of Vulnerabilities and Security Unit Tests}, 
+    author={Emanuele Iannone and Quang-Cuong Bui and Riccardo Scandariato},
+    year={2026},
+    eprint={2502.03365},
+    archivePrefix={arXiv},
+    primaryClass={cs.SE},
+    url={https://arxiv.org/abs/2502.03365}, 
+}
+```
+
+## Model Card Authors
+
+[emaiannone](https://huggingface.co/emaiannone)