RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair

• URL: http://arxiv.org/abs/2312.15698v4
• Date: Fri, 7 Jun 2024 13:21:26 GMT
• Title: RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair
• Authors: André Silva, Sen Fang, Martin Monperrus,
• Abstract summary: We propose RepairLLaMA, a novel program repair approach that identifies optimal code representations for APR with fine-tuned models. This results in a highly effective program repair adapter' for fixing bugs with AI. Overall, RepairLLaMA correctly fixes 144 Defects4J v2 and 109 HumanEval-Java bugs, outperforming all baselines.
• Score: 8.321263361036808
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated Program Repair (APR) has evolved significantly with the advent of Large Language Models (LLMs). Fine-tuning LLMs for program repair is a recent avenue of research, with many dimensions which have not been explored. Existing work mostly fine-tune LLMs with naive code representations and does not scale to frontier models. To address this problem, we propose RepairLLaMA, a novel program repair approach that 1) identifies optimal code representations for APR with fine-tuned models, and 2) pioneers state-of-the-art parameter-efficient fine-tuning technique (PEFT) for program repair. This results in RepairLLaMA producing a highly effective `program repair adapter' for fixing bugs with AI. Our experiments demonstrate the validity of both concepts. First, fine-tuning adapters with program repair specific code representations enables the model to use meaningful repair signals and produce better patches. Second, parameter-efficient fine-tuning helps fine-tuning to converge and clearly contributes to the effectiveness of RepairLLaMA in fixing bugs outside the fine-tuning data distribution. Overall, RepairLLaMA correctly fixes 144 Defects4J v2 and 109 HumanEval-Java bugs, outperforming all baselines.

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