# COAST

**Repository Path**: ccran/COAST

## Basic Information

- **Project Name**: COAST
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-06-18
- **Last Updated**: 2025-06-27

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

![image](https://github.com/NEUIR/COAST/blob/main/Figure/title.png) 

<p align="center">
    <a href="https://arxiv.org/pdf/2408.05006">📜 Paper</a> •
    <a href="https://huggingface.co/datasets/yangweiqing/DebugEval">🤗 Data </a> •
    <a href="https://huggingface.co/yangweiqing">🤖 Model </a>
</p>

## 1. Introduction
This paper presents a benchmark, DebugEval, which is used to evaluate the code debugging ability of LLMs (Large Language Models) and proposals a framework for synthesizing training data using multiple agents, COAST.

### 1.1 DEBUGEVAL
#### DebugEval designs four task scenarios: BUG Localization, BUG Identification, Code Repair, and Code Recognition to comprehensively evaluate the code debugging capability of LLMs.

![image](https://github.com/NEUIR/COAST/blob/main/Figure/benchmark_00.png).
### 1.2 COAST
#### COAST is a framework for making use of multiple agents working together to synthesize training data to improve code debugging capability of LLMs.

![image](https://github.com/NEUIR/COAST/blob/main/Figure/COAST_00.png).
## 2. Installation
You can clone the repository using the following command:

```
git clone https://github.com/NEUIR/COAST
cd COAST
```

## 3. Inference and Evaluation
Download the dataset we provide.

```
cd src
```
Please refer to `src/README.md` for more details.
## 4. Fine-Tuning
We use DeepSeek-Coder-6.7B-Ins and Llama3-8B-Ins as the base model, and train the models with COAST framework.

### 4.1 For DeepSeek-Coder-6.7B-Ins
```
cd neural_compiler
```
Please refer to `neural_compiler/README.md` for more details.
### 4.2 For Llama3-8B-Ins
```
cd LLaMA-Factory
```
Please refer to `LLaMA-Factory/README.md` for more details.

We provide the trained NeuDebugger models.

## 5. Result

![image](https://github.com/NEUIR/DebugEval/blob/main/Figure/performance_00.png)
## 6. Citation
Please cite the paper and star the repo if you use DebugEval and find it helpful.

Feel free to contact 2301983@stu.neu.edu.cn or open an issue if you have any questions.
```
@misc{yang2025coastenhancingcodedebugging,
      title={COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis}, 
      author={Weiqing Yang and Hanbin Wang and Zhenghao Liu and Xinze Li and Yukun Yan and Shuo Wang and Yu Gu and Minghe Yu and Zhiyuan Liu and Ge Yu},
      year={2025},
      eprint={2408.05006},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2408.05006}, 
}
```