Empirical study across five LLMs and four languages finds security-aware prompting changes CWE category distributions but yields no statistically significant reduction in vulnerability frequency or density.
arXiv preprint arXiv:2302.07248 , year=
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RisCoSet applies multiple hypothesis testing to construct risk-controlling partial-program prediction sets for LLM code generation, achieving up to 24.5% less code removal than prior methods at equivalent risk levels.
In a randomized experiment with 97 graduate students, deferred AI assistance produced the highest-quality hints and helped students spot more code mistakes than independent writing or immediate AI help.
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Empirical study across five LLMs and four languages finds security-aware prompting changes CWE category distributions but yields no statistically significant reduction in vulnerability frequency or density.
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