AutoDev: Automated AI-Driven Development
- URL: http://arxiv.org/abs/2403.08299v1
- Date: Wed, 13 Mar 2024 07:12:03 GMT
- Title: AutoDev: Automated AI-Driven Development
- Authors: Michele Tufano, Anisha Agarwal, Jinu Jang, Roshanak Zilouchian
Moghaddam, Neel Sundaresan
- Abstract summary: AutoDev is a fully automated AI-driven software development framework.
It enables users to define complex software engineering objectives, which are assigned to AutoDev's autonomous AI Agents.
AutoDev establishes a secure development environment by confining all operations within Docker containers.
- Score: 9.586330606828643
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The landscape of software development has witnessed a paradigm shift with the
advent of AI-powered assistants, exemplified by GitHub Copilot. However,
existing solutions are not leveraging all the potential capabilities available
in an IDE such as building, testing, executing code, git operations, etc.
Therefore, they are constrained by their limited capabilities, primarily
focusing on suggesting code snippets and file manipulation within a chat-based
interface. To fill this gap, we present AutoDev, a fully automated AI-driven
software development framework, designed for autonomous planning and execution
of intricate software engineering tasks. AutoDev enables users to define
complex software engineering objectives, which are assigned to AutoDev's
autonomous AI Agents to achieve. These AI agents can perform diverse operations
on a codebase, including file editing, retrieval, build processes, execution,
testing, and git operations. They also have access to files, compiler output,
build and testing logs, static analysis tools, and more. This enables the AI
Agents to execute tasks in a fully automated manner with a comprehensive
understanding of the contextual information required. Furthermore, AutoDev
establishes a secure development environment by confining all operations within
Docker containers. This framework incorporates guardrails to ensure user
privacy and file security, allowing users to define specific permitted or
restricted commands and operations within AutoDev. In our evaluation, we tested
AutoDev on the HumanEval dataset, obtaining promising results with 91.5% and
87.8% of Pass@1 for code generation and test generation respectively,
demonstrating its effectiveness in automating software engineering tasks while
maintaining a secure and user-controlled development environment.
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