Exploring Parameter-Efficient Fine-Tuning of Large Language Model on Automated Program Repair
Year: 2024
Type: conference-paper
Abstract: Automated Program Repair (APR) aims to fix bugs by generating patches. And existing work has demonstrated that "pre-training and fine-tuning" paradigm enables Large Language Models (LLMs) improve fixing capabilities on APR. However, existing work mainly focuses on Full-Model Fine-Tuning (FMFT) for APR and limited research has been conducted on the execution-based evaluation of Parameter-Efficient Fine-Tuning (PEFT) for APR. Comparing to FMFT, PEFT can reduce computing resource consumption without compromising performance and has been widely adopted to other software engineering tasks.
Authors: Guochang Li, Chen Zhi, Jialiang Chen, Junxiao Han, Shuiguang Deng
Institutions: Zhejiang University, Hangzhou City University
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403536352