Quality and Security Signals in AI-Generated Python Refactoring Pull Requests
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 using PyQu, an ML-based quality assessment tool for Python, to quantify changes across five quality attributes, and we complement PyQu with domain-ind...
| Authors | Mohamed Almukhtar, Anwar Ghammam, Hua Ming |
|---|---|
| Primary category | cs.SE |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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