arXiv:2603.04177v2 Announce Type: replace-cross Abstract: LLM coding agents can generate working code, but their solutions often accumulate complexity, duplication, and architectural debt. Human developers address such issues through refactoring: behavior-preserving program transformations that improve structure and maintainability. We investigate whether agents (i) can execute refactorings reliably and (ii) identify the refactorings that human developers actually chose in real codebases. To this end, we construct CodeTaste, a benchmark mined from large multi-file open-source refactorings. To
The rapid advancement and deployment of Large Language Models (LLMs) have shifted focus from basic code generation to more complex, human-like programming tasks such as refactoring.
This research explores a critical frontier for AI agents, determining their ability to not only write code but also to improve its quality, maintainability, and architectural soundness, which directly impacts software development efficiency and cost.
The potential for AI agents to perform human-level code refactoring could fundamentally change software development workflows, increasing developer productivity and code-base health.
- · Software Development Teams
- · Cloud Computing Providers
- · AI Development Platforms
- · Junior Software Engineers (task displacement)
Increased efficiency in software maintenance and development cycles.
Reduced technical debt in long-lived software projects, leading to more stable and scalable systems.
Evolution of developer roles, shifting focus from routine refactoring to higher-level design and architectural oversight, potentially leading to new forms of human-AI collaboration.
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Read at arXiv cs.LG