arXiv:2605.21453v1 Announce Type: cross Abstract: As AI agents increasingly contribute to code development and maintenance, there is still limited empirical evidence on the quality and risk characteristics of their changes in real-world projects, particularly for refactoring-oriented contributions. It remains unclear how agent-authored refactoring edits affect maintainability, code quality, and security once merged into GitHub repositories. To address this gap, we conduct an empirical study of Python refactoring pull requests (PRs) from the AIDev dataset. We analyze agentic refactoring PRs usi
As AI agents become more sophisticated and integrated into software development, empirical studies are crucial to understand their practical impact on code quality and security.
This research provides critical insights into the real-world implications of AI-generated code, directly influencing trust, adoption, and investment in AI agent development.
The understanding of AI agents' reliability and potential risks in code refactoring becomes clearer, informing deployment strategies and best practices.
- · AI agent developers (with robust quality control)
- · Software quality assurance sector
- · Cybersecurity sector
- · Open-source projects adopting AI agents responsibly
- · AI agent developers (without robust quality control)
- · Projects indiscriminately integrating AI-generated code
- · Manual refactoring as AI improves
Increased empirical scrutiny of AI agent performance in real-world software engineering tasks.
Development of new tools and methodologies to audit and assure the quality and security of AI-generated code.
A shift in software development workflows, with AI agents handling more complex and critical refactoring tasks, leading to changes in developer roles.
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Read at arXiv cs.AI