A within-subject study of 12 developers found that security training reduced validated weaknesses by 31.5% and critical issues by 79.2% in LLM-assisted backend coding.
Devaic: A tool for security assessment of ai-generated code
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PEFT fine-tuning of Code Llama yields feedback on student Java bugs that students judge equal to ChatGPT and better than prompt engineering, using BLEU/ROUGE/BERTScore plus human ratings.
Comparative review of AI coding tool ToS shows responsibility for code quality and compliance shifted to users, with policy misalignment for autonomous agents, plus a research roadmap.
citing papers explorer
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A Quasi-Experimental Developer Study of Security Training in LLM-Assisted Web Application Development
A within-subject study of 12 developers found that security training reduced validated weaknesses by 31.5% and critical issues by 79.2% in LLM-assisted backend coding.
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Fine-Tuning Models for Automated Code Review Feedback
PEFT fine-tuning of Code Llama yields feedback on student Java bugs that students judge equal to ChatGPT and better than prompt engineering, using BLEU/ROUGE/BERTScore plus human ratings.
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Accountable Agents in Software Engineering: An Analysis of Terms of Service and a Research Roadmap
Comparative review of AI coding tool ToS shows responsibility for code quality and compliance shifted to users, with policy misalignment for autonomous agents, plus a research roadmap.