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🕷️DebugEval

Official repository for the paper "Enhancing the Code Debugging Ability of LLMs via Communicative Agent Based Data Refinement".

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 building training data using multiple agents, MASTER.

1.1 Benchmark

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

1.2 MASTER

MASTER is a framework for making use of multiple agents working together to refine training data to improve code debugging capability in LLMs.

2. Installation

You can clone the repository using the following command:

git clone DebugEval
cd DebugEval

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 MASTER 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

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.

@article{DebugEval2024,
      title={Enhancing the Code Debugging Ability of LLMs via Communicative Agent Based Data Refinement}, 
      author={Weiqing Yang, Hanbin Wang, Zhenghao Liu, Xinze Li, Yukun Yan, Shuo Wang, Yu Gu, Minghe Yu, Zhiyuan Liu and Ge Yu},
      year={2024},
}