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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 using PyQu, an ML-based quality assessment tool for Python, to quantify changes across five quality attributes, and we complement PyQu with domain-independent static analysis (Pylint and Bandit) to measure code quality and security issues before and after each change.\n  Our results show that, on average, agentic commits improve a quality attribute in 22.5% of the studied changes, with usability impr","title":"Quality and Security Signals in AI-Generated Python Refactoring Pull Requests","url":"https://arxiv.org/abs/2605.21453","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.21453v1 Announce Type: cross \nAbstract: 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. 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