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

• URL: http://arxiv.org/abs/2411.02462v1
• Date: Mon, 04 Nov 2024 09:03:18 GMT
• Title: Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation: An Empirical Study
• Authors: André Storhaug, Jingyue Li,
• Abstract summary: Large language models (LLMs) like GitHub Copilot struggle with real-world tasks without fine-tuning. This paper investigates full fine-tuning and various PEFT methods, including LoRA, (IA)3, and prompt tuning. Our findings show that PEFT methods can deliver performance comparable to full fine-tuning for unit test generation.
• Score: 3.5189934649278922
• License:
• Abstract: The advent of large language models (LLMs) like GitHub Copilot has significantly enhanced programmers' productivity, particularly in code generation. However, these models often struggle with real-world tasks without fine-tuning. As LLMs grow larger and more performant, fine-tuning for specialized tasks becomes increasingly expensive. 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 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 application of PEFT techniques in unit test generation remains underexplored. The state-of-the-art is limited to using LLMs with full fine-tuning to generate unit tests. This paper investigates both full fine-tuning and various PEFT methods, including LoRA, (IA)^3, and prompt tuning, across different model architectures and sizes. We use well-established benchmark datasets to evaluate their effectiveness in unit test generation. Our findings show that PEFT methods can deliver performance comparable to full fine-tuning for unit test generation, making specialized fine-tuning more accessible and cost-effective. Notably, prompt tuning is the most effective in terms of cost and resource utilization, while LoRA approaches the effectiveness of full fine-tuning in several cases.

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