✨Agentic Refactoring: An Empirical Study of AI Coding Agents

📝 Summary:
A study of AI agent-generated refactoring in Java projects found agents frequently perform low-level consistency edits. Driven by maintainability and readability, these refactorings lead to small but significant improvements in code quality metrics like class size and complexity.

🔹 Publication Date: Published on Nov 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.04824
• PDF: https://arxiv.org/pdf/2511.04824

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#AIagents #CodeRefactoring #SoftwareEngineering #CodeQuality #AIResearch

✨Agent READMEs: An Empirical Study of Context Files for Agentic Coding

📝 Summary:
This study analyzed 2303 agent context files, finding them complex and evolving like config code. Developers prioritize functional details but rarely specify non-functional requirements like security or performance. This suggests a gap in guardrails for agent-written code quality.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.12884
• PDF: https://arxiv.org/pdf/2511.12884

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For more data science resources:
✓ https://t.iss.one/DataScienceT

#AIAgents #SoftwareEngineering #CodeQuality #LLMs #AIResearch