Researchers Evaluate Models Assisting Code Migration with 90.7% Validation

Javier Pallarés de Bonrostro and colleagues at the Computer Science and Engineering Department, in collaboration with the Mathematics Department, have developed a system using large language models to automate a key step in the transition to post-quantum cryptography. They present a reproducible framework and a dataset of 800 paired Python code fragments to evaluate the ability of LLMs to migrate vulnerable cryptographic code to post-quantum alternatives while maintaining functionality. Results reveal that fine-tuning is essential, with a fine-tuned GPT-4.1-mini model achieving 92.5% functional correctness and a mean static similarity of 0.9072, a sharp improvement over zero-shot performance. The system suggests that, when combined with automated verification, fine-tuned LLMs…