File size: 3,740 Bytes
596fa1b 21c0d7a 596fa1b 21c0d7a 596fa1b 21c0d7a 596fa1b 21c0d7a 596fa1b 21c0d7a 596fa1b 21c0d7a 596fa1b 21c0d7a 596fa1b 21c0d7a 596fa1b 21c0d7a 596fa1b 21c0d7a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
---
library_name: transformers
datasets:
- NamCyan/tesoro-code
base_model:
- Salesforce/codet5p-220m
---
# Improving the detection of technical debt in Java source code with an enriched dataset
## Model Details
### Model Description
This model is the part of Tesoro project, used for detecting technical debt in source code. More information can be found at [Tesoro HomePage](https://github.com/NamCyan/tesoro.git).
- **Developed by:** [Nam Hai Le](https://github.com/NamCyan)
- **Model type:** Encoder-Decoder-based PLMs
- **Language(s):** Java
- **Finetuned from model:** [CodeT5+](https://huggingface.co/Salesforce/codet5p-220m)
### Model Sources
- **Repository:** [Tesoro](https://github.com/NamCyan/tesoro.git)
- **Paper:** [To be update]
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("NamCyan/codet5p-220m-technical-debt-code-tesoro")
model = AutoModelForSequenceClassification.from_pretrained("NamCyan/codet5p-220m-technical-debt-code-tesoro")
```
## Training Details
- Training Data: The model is finetuned using [tesoro-code](https://huggingface.co/datasets/NamCyan/tesoro-code)
- Infrastructure: Training process is conducted on two NVIDIA A100 GPUs with 80GB of VRAM.
## Leaderboard
| Model | Model size | EM | F1 |
|:-------------|:-----------|:------------------|:------------------|
| **Encoder-based PLMs** |
| [CodeBERT](https://huggingface.co/microsoft/codebert-base) | 125M | 38.28 | 43.47 |
| [UniXCoder](https://huggingface.co/microsoft/unixcoder-base) | 125M | 38.12 | 42.58 |
| [GraphCodeBERT](https://huggingface.co/microsoft/graphcodebert-base)| 125M | *39.38* | *44.21* |
| [RoBERTa](https://huggingface.co/FacebookAI/roberta-base) | 125M | 35.37 | 38.22 |
| [ALBERT](https://huggingface.co/albert/albert-base-v2) | 11.8M | 39.32 | 41.99 |
| **Encoder-Decoder-based PLMs** |
| [PLBART](https://huggingface.co/uclanlp/plbart-base) | 140M | 36.85 | 39.90 |
| [Codet5](https://huggingface.co/Salesforce/codet5-base) | 220M | 32.66 | 35.41 |
| [CodeT5+](https://huggingface.co/Salesforce/codet5p-220m) | 220M | 37.91 | 41.96 |
| **Decoder-based PLMs (LLMs)** |
| [TinyLlama](https://huggingface.co/TinyLlama/TinyLlama_v1.1_math_code) | 1.03B | 37.05 | 40.05 |
| [DeepSeek-Coder](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) | 1.28B | **42.52** | **46.19** |
| [OpenCodeInterpreter](https://huggingface.co/m-a-p/OpenCodeInterpreter-DS-1.3B) | 1.35B | 38.16 | 41.76 |
| [phi-2](https://huggingface.co/microsoft/phi-2) | 2.78B | 37.92 | 41.57 |
| [starcoder2](https://huggingface.co/bigcode/starcoder2-3b) | 3.03B | 35.37 | 41.77 |
| [CodeLlama](https://huggingface.co/codellama/CodeLlama-7b-hf) | 6.74B | 34.14 | 38.16 |
| [Magicoder](https://huggingface.co/ise-uiuc/Magicoder-S-DS-6.7B) | 6.74B | 39.14 | 42.49 |
## Citing us
```bibtex
@article{nam2024tesoro,
title={Improving the detection of technical debt in Java source code with an enriched dataset},
author={Hai, Nam Le and Bui, Anh M. T. Bui and Nguyen, Phuong T. and Ruscio, Davide Di and Kazman, Rick},
journal={},
year={2024}
}
``` |