Continuous Autonomous Refactoring: A Research Roadmap for AI-Driven Code Quality Maintenance

2026-09-01

AI Summary

本文提出利用大型语言模型进行持续代码重构，通过五个维度解决软件质量维护问题，并分析了相关挑战和研究问题。

Abstract

Large language models have shown promising capabilities in code refactoring, but existing approaches remain limited to method-level tasks. In this paper, we envision LLM-based refactoring as a continuous component of software maintenance rather than a tool invoked only for occasional manual refactoring. Under this vision, AI agents continuously monitor, evaluate, and improve codebases against explicit and evolving notions of software quality. We present a roadmap organized around five dimensions: the multi-objective optimization problem, quality definition and evaluation, multi-timescale integration of heterogeneous signals, architecture and design pattern, and trust in autonomous refactoring. We further identify integration into continuous delivery pipelines and cost considerations as cross-cutting concerns. For each dimension, we analyze the underlying challenges and pose open research questions. These dimensions define a research agenda for advancing autonomous refactoring from isolated code improvements to system-level quality maintenance.

Problem

• Continuous Autonomous Refactoring
• AI-Driven Code Quality Maintenance
• Large Language Models

Innovation

• Continuous Autonomous Refactoring
• Multi-objective Optimization
• Quality Definition and Evaluation