Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation: An Empirical Study

Understand

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 language models (LLMs) while maintaining their performance.

• Existing studies have explored using PEFT and LLMs for various code-related tasks and found that the effectiveness of PEFT techniques is task-dependent.
• The state-of-the-art is limited to using LLMs with full fine-tuning to generate unit tests.
• The application of PEFT techniques in unit test generation remains underexplored.