codereviewer

Introduction

CodeReviewer is a model designed for automating code review tasks. It has been pre-trained using a dataset of code changes and code reviews, enabling it to assist in code review activities effectively.

Architecture

The CodeReviewer model is structured to support text-to-text generation, leveraging the capabilities of transformers and PyTorch. It is compatible with T5 models and can be used for text generation inference. The model aims to enhance code review processes by providing automated insights and suggestions.

Training

The model has been pre-trained on a large dataset that includes code change and review data. This pre-training allows CodeReviewer to understand the context and nuances of code reviews, making it a valuable tool for developers. The model's capabilities are detailed in the research paper titled "CodeReviewer: Pre-Training for Automating Code Review Activities," available on arXiv.

Guide: Running Locally

1. Setup Environment: Ensure you have Python and PyTorch installed. It is recommended to use a virtual environment to manage dependencies.
2. Clone Repository: Access the CodeReviewer repository on GitHub and clone it to your local machine.
3. Install Dependencies: Navigate to the cloned directory and run the package installation commands, typically using pip.
4. Run Inference: Use the provided scripts to perform code review tasks. Modify the script parameters as needed for your specific use case.
5. Utilize Cloud GPUs: For enhanced performance, consider using cloud-based GPU services like AWS, Google Cloud, or Azure, which can significantly speed up model inference and training processes.

License

CodeReviewer is released under the Apache 2.0 License, allowing for wide usage and modification in both personal and commercial projects.