|
|
--- |
|
|
language: |
|
|
- en |
|
|
- zh |
|
|
tags: |
|
|
- uml |
|
|
- software-engineering |
|
|
- requirements-modeling |
|
|
- glm4 |
|
|
- code-generation |
|
|
license: apache-2.0 |
|
|
--- |
|
|
|
|
|
# GLM4UML |
|
|
|
|
|
GLM4UML is a fine-tuned version of GLM4-9B specifically designed for generating UML diagrams from natural language requirements. It supports class diagrams, use case diagrams, and sequence diagrams generation with high accuracy. |
|
|
|
|
|
## Model Description |
|
|
|
|
|
- **Model Type:** Fine-tuned GLM4-9B |
|
|
- **Language(s):** English and Chinese |
|
|
- **Task:** UML Diagram Generation |
|
|
- **License:** Apache 2.0 |
|
|
|
|
|
### Training Data |
|
|
|
|
|
The model was trained on a comprehensive dataset of software requirements and their corresponding UML diagrams, focusing on: |
|
|
|
|
|
- Class diagrams |
|
|
- Use case diagrams |
|
|
- Sequence diagrams |
|
|
|
|
|
## Performance and Limitations |
|
|
|
|
|
### Metrics |
|
|
|
|
|
The model achieves the following performance metrics: |
|
|
|
|
|
- Precision: 78.68% |
|
|
- Recall: 67.37% |
|
|
- F1 Score: 72.59% |
|
|
|
|
|
Compared to ChatGPT 4.0, AUG shows improved performance in: |
|
|
|
|
|
- Recall rate |
|
|
- Overall F1 score |
|
|
|
|
|
### Limitations |
|
|
|
|
|
While the model performs well, users should be aware that: |
|
|
|
|
|
- The model's output may still require human verification |
|
|
- Complex software architectures might need additional refinement |
|
|
- Performance may vary based on the clarity of input requirements |
|
|
|
|
|
## Training Procedure |
|
|
|
|
|
The model was fine-tuned from GLM4-9B using: |
|
|
|
|
|
- Specialized UML diagram dataset |
|
|
- Requirements-to-diagram mapping |
|
|
- Clarification dialogue patterns |
|
|
|
|
|
## Additional Information |
|
|
|
|
|
- **GitHub Repository:** https://github.com/XIAOLingQ/AUG |
|
|
- **Demo Video:** https://youtu.be/kHbCPK6kOag |
|
|
|
|
|
## Acknowledgements |
|
|
|
|
|
This model was developed by researchers from Wuhan Textile University and Wuhan University. Special thanks to all contributors and the open-source community. |
