# AutoDev: Automated AI-Driven Development

**Authors:** Michele Tufano, Anisha Agarwal, Jinu Jang, Roshanak Zilouchian Moghaddam, Neel Sundaresan  
**arXiv:** [2403.08299](https://arxiv.org/abs/2403.08299)  
**Date:** 2024-03-13  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2403.08299v1/

## Key points

- AutoDev is an AI-driven software development framework aiming to revolutionize software engineering by automating tasks within IDEs.
- Unlike GitHub Copilot, AutoDev allows users to define complex software engineering objectives executed autonomously by AI Agents.
- AI Agents in AutoDev can perform various operations on a codebase such as file editing, retrieval, build processes, execution, testing, and git operations.
- AutoDev leverages contextual information in a secure Docker container environment with guardrails for user control and privacy protection.
- In an evaluation using the HumanEval dataset, AutoDev showed promising results with 91.5% and 87.8% Pass@1 rates for code generation and test generation respectively.
- The effectiveness of AutoDev lies in automating software engineering tasks while maintaining a secure and user-controlled development environment.
- With technologies like AutoDev's autonomous planning and execution capabilities utilizing Large Language Models (LLMs), developers can achieve their goals more efficiently in the field of software engineering.

## Summary

AutoDev is an innovative AI-driven software development framework that aims to revolutionize the landscape of software engineering by automating intricate tasks within Integrated Development Environments (IDEs). Unlike existing solutions like GitHub Copilot, which primarily focus on suggesting code snippets and file manipulation, AutoDev goes beyond by enabling users to define complex software engineering objectives that are then executed autonomously by AI Agents. These agents can perform a wide range of operations on a codebase, including file editing, retrieval, build processes, execution, testing, and git operations. By leveraging contextual information such as files, compiler output, build and testing logs, and static analysis tools in a secure Docker container environment with guardrails for user control and privacy protection. In an evaluation using the HumanEval dataset, AutoDev demonstrated promising results with 91.5% and 87.8% Pass@1 rates for code generation and test generation respectively. This showcases the effectiveness of AutoDev in automating software engineering tasks while maintaining a secure and user-controlled development environment. As software development practices continue to evolve with the integration of cutting-edge technologies like AutoDev's autonomous planning and execution capabilities utilizing Large Language Models (LLMs), developers can now achieve their goals more efficiently and effectively in the ever-evolving field of software engineering.