MORepair: Teaching LLMs to Repair Code via Multi-Objective Fine-tuning

TL;DR

MORepair introduces a multi-objective fine-tuning approach for LLMs that enhances code repair by focusing on both syntactic patterns and logical reasoning behind code changes, significantly improving repair performance.

Contribution

It proposes a novel multi-objective fine-tuning method that incorporates logical reasoning into LLM training for program repair, outperforming existing approaches.

Findings

Boosts repair performance by up to 56%.

Outperforms state-of-the-art fine-tuning methods.

Effective across different LLM architectures and sizes.

Abstract

Within the realm of software engineering, specialized tasks on code, such as program repair, present unique challenges, necessitating fine-tuning Large language models~(LLMs) to unlock state-of-the-art performance. Fine-tuning approaches proposed in the literature for LLMs on program repair tasks generally overlook the need to reason about the logic behind code changes, beyond syntactic patterns in the data. High-performing fine-tuning experiments also usually come at very high computational costs. With MORepair, we propose a novel perspective on the learning focus of LLM fine-tuning for program repair: we not only adapt the LLM parameters to the syntactic nuances of the task of code transformation (objective 1), but we also specifically fine-tune the LLM with respect to the logical reason behind the code change in the training data (objective 2). Such a multi-objective fine-tuning will instruct LLMs to generate high-quality patches. We apply MORepair to fine-tune four open-source LLMs with different sizes and architectures. Experimental results on function-level and repository-level repair benchmarks show that the implemented fine-tuning effectively boosts LLM repair performance by 11.4% to 56.0%. We further show that our fine-tuning strategy yields superior performance compared to the state-of-the-art approaches, including standard fine-tuning, Fine-tune-CoT, and RepairLLaMA.