✨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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#AIAgents #SoftwareEngineering #CodeQuality #LLMs #AIResearch

✨SciCoQA: Quality Assurance for Scientific Paper--Code Alignment

📝 Summary:
SciCoQA is a dataset containing 611 paper-code discrepancies for identifying mismatches between scientific publications and code. It shows that even advanced language models struggle significantly to detect these issues, with the best model finding less than half of real-world discrepancies.

🔹 Publication Date: Published on Jan 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.12910
• PDF: https://arxiv.org/pdf/2601.12910
• Project Page: https://ukplab.github.io/scicoqa
• Github: https://github.com/UKPLab/scicoqa

✨ Datasets citing this paper:
• https://huggingface.co/datasets/UKPLab/scicoqa

✨ Spaces citing this paper:
• https://huggingface.co/spaces/UKPLab/scicoqa

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#SciCoQA #AcademicIntegrity #CodeQuality #NLP #ResearchData

✨Investigating Autonomous Agent Contributions in the Wild: Activity Patterns and Code Change over Time

📝 Summary:
Researchers analyzed AI coding agent contributions to open source projects. They found increasing agent activity but higher code churn over time compared to human-authored code.

🔹 Publication Date: Published on Apr 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.00917
• PDF: https://arxiv.org/pdf/2604.00917
• Project Page: https://arxiv.org/html/2604.00917v1

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#AIAgents #SoftwareEngineering #OpenSource #CodeQuality #AIResearch