| --- |
| title: README |
| emoji: 📚 |
| colorFrom: indigo |
| colorTo: yellow |
| sdk: static |
| pinned: false |
| --- |
| |
| # OSS-Forge |
|
|
| **OSS-Forge** is an open research initiative focused on *trustworthy, secure, and transparent AI-assisted software engineering*. |
| We develop and publish: |
|
|
| - **static and dynamic analyzers** for AI-generated code |
| - **benchmarks and datasets** for software vulnerabilities, defects, exploits, and shellcode |
| - **evaluation frameworks** for correctness, robustness, and data poisoning |
| - **models and reproducible pipelines** for secure code generation |
| - **experimental tools and artifacts** from peer-reviewed scientific publications |
|
|
| Our mission is to build a transparent, verifiable, and secure ecosystem for integrating Large Language Models (LLMs) into software development, especially in safety-critical and security-sensitive contexts. |
|
|
| --- |
|
|
| ## What You Will Find Here |
|
|
| This organization hosts resources from multiple research projects and publications in AI security, software engineering, and code generation. Current categories include: |
|
|
| ### Static Analyzers & Security Tools |
| - **DeVAIC** – Fast static analysis for detecting vulnerabilities in Python code |
| - **PatchitPy** – Automated patching of vulnerable Python code via pattern-based transformations |
| - **ACCA** – Automated correctness assessment of AI-generated code using symbolic execution |
|
|
| ### Datasets for Security & Software Engineering |
| - **PyResBugs** – 5,007 residual Python bugs with NL descriptions |
| - **Shellcode_IA32** – The largest curated dataset of IA-32 shellcode snippets |
| - **PoisonPy** – Dataset supporting targeted data-poisoning attacks |
| - **Human vs AI Code** – Defects, vulnerabilities, and complexity analysis at scale |
| |
| ### Robustness, Data Quality & Industrial Code Generation |
| - **Residual Bug Generation from Natural Language** – Frameworks for generating realistic residual defects from NL descriptions |
| - **Impact of Data Quality on Code Models** – Empirical studies on robustness, poisoning resilience, and dataset quality |
| - **Industrial Code Generation** – Models for domain-specific code synthesis (e.g., VHDL generation from natural language) |
| |
| Our repositories include code, experimental scripts, datasets, and reproducibility materials. |
| |
| --- |
| |
| ## Research Themes |
| |
| Our work spans four interconnected areas: |
| |
| 1. **Security of AI-generated Code** |
| Vulnerability detection, automated patching, exploit generation, and robustness testing. |
| |
| 2. **Trustworthy LLM Evaluation** |
| Correctness, equivalence checking, symbolic execution, reproducible benchmarks. |
| |
| 3. **Software Engineering with AI** |
| Defect analysis, complexity metrics, orthogonal defect classification (ODC). |
| |
| 4. **Adversarial ML for Code Models** |
| Data poisoning, robustness stress-testing, unsafe pattern injection. |
| |
| All research artifacts are peer-reviewed and associated with publications at DSN, ISSRE, ICPC, IST, EMSE, JSS, AUSE, and other venues. |
| |
| --- |
| |
| ## Publications Powered by These Repositories |
| |
| A non-exhaustive list includes works presented at: |
| |
| - **IEEE/IFIP DSN** |
| - **IEEE ISSRE** |
| - **IEEE/ACM ICPC** |
| - **Empirical Software Engineering (EMSE)** |
| - **Information and Software Technology (IST)** |
| - **Automated Software Engineering (AUSE)** |
| - **Journal of Systems and Software (JSS)** |
| |
| Full references are available inside each corresponding repository. |
| |
| --- |
| |
| ## Contributing |
| |
| We encourage contributions from the research and practitioner community. |
| |
| You can contribute by: |
| |
| - submitting new datasets |
| - improving static analysis rules |
| - adding benchmarks or experimental scripts |
| - reporting issues or proposing new features |
| |
| Please open discussions or pull requests inside the relevant repository. |
| |
| --- |
| |
| ## Contact |
| |
| OSS-Forge is developed by a joint research team from the **University of North Carolina at Charlotte (UNCC)** and the **University of Naples Federico II**. |
| |
| ### Scientific Leadership |
| - Prof. [Domenico Cotroneo](https://webpages.charlotte.edu/dcotrone/) — UNCC |
| |
| ### Core Research Contributors |
| - Dr. [Pietro Liguori](http://wpage.unina.it/pietro.liguori/) — University of Naples Federico II |
| - [Cristina Improta](http://wpage.unina.it/cristina.improta/) — University of Naples Federico II |
| - Ph.D. students and graduate researchers and contributors from the DESSERT Research group — University of Naples Federico II |
| |
| --- |
