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cs.SE2025
An Exploratory Study of Bayesian Prompt Optimization for Test-Driven Code Generation with Large Language Models
Shlok Tomar, Aryan Deshwal, Ethan Villalovoz +3
We consider the task of generating functionally correct code using large language models (LLMs). The correctness of generated code is influenced by the prompt used to query the giv…
cs.SE2025
DualGauge: Automated Joint Security-Functionality Benchmarking of Specification-Only Code Generation by LLMs and Coding Agents
Rupam Patir, Keyan Guo, Suvadra Barua +5
Large language models (LLMs) and LLM-based coding agents are now used to generate code from natural-language specifications, yet ensuring such code is both functionally correct and…