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+ 📖Overview • + 🧪Environment • + 🚀Quick Start • + 📝Citation +
+ + +## 📖Overview -## Overview In this paper, we draw on pair programming practices to propose **PairCoder**, a novel LLM-based framework for code generation. PairCoder incorporates two collaborative LLM agents, namely a *Navigator* agent for high-level planning and a *Driver* agent for specific implementation. The *Navigator* is responsible for proposing promising solution plans, selecting the current optimal plan, and directing the next iteration round based on execution feedback. The *Driver* follows the guidance of *Navigator* to undertake initial code generation, code testing, and refinement. This interleaved and iterative workflow involves multi-plan exploration and feedback-based refinement, which mimics the collaboration of pair programmers. +  -## Prepare Environment + +## 🧪Prepare Environment PairCoder is developed on Ubuntu 16.04 LTS. Please follow these steps to set up the Python environment: -``` +```bash conda create -n PairCoder python=3.10 conda activate PairCoder pip install -r requirements.txt @@ -21,28 +38,29 @@ pip install -r requirements.txt Please set your API KEY in `settings/configuration.toml`. This file also contains numerous other configurable options that allow you to fine-tune and precisely control the behavior of PairCoder. -## Quick Start +## 🚀Quick Start Use the following command to perform code generation: -``` +```bash python src/solve_dataset.py \ - --dataset_name mbpp \ + --dataset_name mbpp \ --split_name test \ --dir_path results ``` -For the given `$split_name` of the `$dataset_name`, the logs and the final `solutions.json` are stored in `$dir_path`. You can set `$id_list` for ids to solve. +For the given `split_name` of the `dataset_name`, the logs and the final `solutions.json` are stored in `dir_path`. You can set `id_list` for ids to solve. -## Reference +## 📝Citation If you find the code helpful, please cite our paper: -``` + +```bibtex @inproceedings{zhang2024paircoder, - title = { A Pair Programming Framework for Code Generation via - Multi-Plan Exploration and Feedback-Driven Refinement }, - author = { Zhang, Huan and Cheng, Wei and Wu, Yuhan and Hu, Wei }, - booktitle = { ASE }, - year = { 2024 } + title = {A Pair Programming Framework for Code Generation via + Multi-Plan Exploration and Feedback-Driven Refinement}, + author = {Zhang, Huan and Cheng, Wei and Wu, Yuhan and Hu, Wei}, + booktitle = {The 39th IEEE/ACM International Conference on Automated Software Engineering (ASE 2024)}, + year = {2024} } ```