1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.SE2024
Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation: An Empirical Study
André Storhaug, Jingyue Li
Parameter-efficient fine-tuning (PEFT) methods, which fine-tune only a subset of model parameters, offer a promising solution by reducing the computational costs of tuning large la…
cs.CR2023★ 1 cited
Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding
André Storhaug, Jingyue Li, Tianyuan Hu
Auto-completing code enables developers to speed up coding significantly. Recent advances in transformer-based large language model (LLM) technologies have been applied to code syn…
cs.CY2023
Evaluating the Impact of ChatGPT on Exercises of a Software Security Course
Jingyue Li, Per Håkon Meland, Jakob Svennevik Notland +2
Along with the development of large language models (LLMs), e.g., ChatGPT, many existing approaches and tools for software security are changing. It is, therefore, essential to und…