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

• URL: http://arxiv.org/abs/2609.01236v1
• Date: Tue, 01 Sep 2026 13:35:29 GMT
• Title: Continuous Autonomous Refactoring: A Research Roadmap for AI-Driven Code Quality Maintenance
• Abstract summary: We envision LLM-based pipelines as a continuous component of software maintenance rather than a tool invoked only for occasional manual tasks.<n>Under this vision, AI agents continuously monitor, evaluate, and improves against explicit and evolving notions of software quality.
• Score: 3.0540716731676625
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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