Setting Up AutoCodeRover on Windows

Anas Mohammad -

AutoCodeRover is an AI-powered tool designed to autonomously improve software systems by integrating Large Language Models (LLMs) with advanced code search and repair capabilities.

Whether you're a developer aiming to streamline debugging or an enterprise seeking automated program repair, this guide will walk you through installing and configuring AutoCodeRover on Windows.

Why Use AutoCodeRover?

Before diving into setup, here’s why AutoCodeRover stands out:

  • Autonomous Bug Fixing: Resolve GitHub issues automatically with AI-driven code analysis.
  • Multi-Language Support: Works with Python, Java, C/C++, JavaScript, Go, PHP, and more.
  • LLM Integration: Compatible with commercial and open-source models like GPT-4, Claude, or Llama.
  • Structural Code Analysis: Operates on abstract syntax trees (ASTs) for deeper codebase insights.

Prerequisites

  1. Operating System: Windows 10 or 11 (64-bit).
  2. Hardware: Minimum 4GB RAM, 2GB disk space.
  3. Permissions: Administrator access for service setup.
  4. GitHub Account: Optional for accessing repositories.

Step 1: Download & Install AutoCodeRover

Download the Latest Release

  1. Visit the AutoCodeRover GitHub Releases page.
  2. Download the AutoCodeRover-Windows.zip file for the latest version.

Extract Files

  1. Unzip the package into a dedicated folder (e.g., C:\AutoCodeRover).
  2. Ensure all files are preserved (look for autocoderover.exe).

Step 2: Configure AutoCodeRover

Automatic Configuration

  1. First-Time Setup: Run autocoderover.exe. A default config file (config.yml) will generate in the installation folder.
  2. Edit Defaults (Optional): Adjust settings like:
    • Port: Change from default 7471 if needed.
    • Log Level: Set to debug for detailed logs.
    • LLM Model: Link to your preferred model API (e.g., OpenAI).

Manual Configuration

  1. Create a config.yml file in the installation directory.

Use the official template to customize:

port: 7471  
log_level: info  
llm:  
  api_key: YOUR_API_KEY  
  model: gpt-4

Step 3: Run AutoCodeRover as a Windows Service

To keep AutoCodeRover running in the background:

Using Task Scheduler

  1. Open Task Scheduler (search via Windows Start).
  2. Click Create Basic Task.
  3. Name & Description:
    • Name: AutoCodeRover Service
    • Description: Background service for autonomous code analysis and repair.
  4. Trigger: Select When I log on or At startup.
  5. Action:
    • Choose Start a program.
    • Browse to autocoderover.exe in your installation folder.
  6. Run as Admin:
    • Check Run with highest privileges.
    • Enter your Windows admin password when prompted.
  7. Finalize: Click Finish.

Verify the Service

  1. In Task Scheduler, right-click the task and select Run.
  2. Check the log file (autocoderover.log) for errors.
  3. Open http://localhost:7471 in your browser to confirm the dashboard loads.

Step 4: Advanced Configuration Tips

AutoCodeRover uses AST-based traversal for precise code retrieval. Enhance its behavior in config.yml:

code_search:  
  max_files: 50  # Limit files analyzed per task  
  lang: python    # Prioritize Python projects

Integrate Custom LLMs

  1. Restart the service to apply changes.
  2. Add your model’s API endpoint and key:
llm:  
  api_key: YOUR_CUSTOM_LLM_KEY  
  base_url: https://api.example.com/v1

Troubleshooting

Service Fails to Start

  • Cause: Missing dependencies like .NET Framework 6.0.
  • Fix: Install the latest .NET Runtime.

Connection Errors

  • Ensure firewall rules allow port 7471.
  • Verify the LLM API key is valid.

How to Update?

  1. Download the latest release from GitHub.
  2. Replace files in the installation folder (keep config.yml).

AutoCodeRover’s Capabilities

AutoCodeRover integrates Large Language Models (LLMs) with code search capabilities to autonomously modify or patch programs.

Unlike other LLM-based approaches, AutoCodeRover is designed for software engineering tasks, working with an abstract syntax tree rather than treating a project as just a collection of files. This approach enables deeper issue analysis and code improvement.

Key features include:

  • Autonomous Program Repair: Automates bug fixing and feature addition in software projects.
  • Contextual Code Retrieval: Iteratively explores the codebase, enriching LLMs with contextual knowledge to better understand GitHub issues.
  • Fault Localization: Utilizes debugging techniques, such as test-based fault localization, to guide code searches for resolving issues.
  • Broad Language Support: Compatible with Python, C, C++, JavaScript, TypeScript, Java, PHP, and Go.
  • Model Integration: Supports both commercial and open-source models, allowing users to configure existing LLMs or use fine-tuned models.

Tips for Efficient Setup

For a smoother installation and usage experience, consider these strategies:

  • Understand the Process: Carefully read and comprehend the setup instructions.
  • Create an Outline: Organize your steps before beginning the installation.
  • Use Voice-to-Text Tools: Employ tools like Google Docs Voice Typing or Otter.ai to speed up documentation.
  • Proofread and Edit: Review your configurations and installation steps to ensure accuracy and completeness.

Conclusion

AutoCodeRover is an AI-powered DevAssistant designed to revolutionize how enterprises and developers manage and enhance their software systems through autonomous program improvement.

AutoCodeRover transforms how developers approach debugging and feature implementation by automating code repairs with AI precision.

References

  1. Run DeepSeek Janus-Pro 7B on Mac: A Comprehensive Guide Using ComfyUI
  2. Run DeepSeek Janus-Pro 7B on Mac: Step-by-Step Guide
  3. Run DeepSeek Janus-Pro 7B on Windows: A Complete Installation Guide
  4. Setting Up AutoCodeRover on macOS