Parameter-Efficient Fine-Tuning with Attributed Patch Semantic Graph for Automated Patch Correctness Assessment

TL;DR

This paper introduces a novel graph-based representation and a parameter-efficient fine-tuning method for large language models to improve automated patch correctness assessment in program repair, addressing semantic and attribute capture limitations.

Contribution

It proposes the attributed patch semantic graph (APSG) for better semantic and attribute encoding, and Graph-LoRA for efficient LLM fine-tuning, enhancing patch correctness prediction accuracy.

Findings

Improves accuracy by up to 7.5% over state-of-the-art methods.

Enhances F1 score by up to 7.1%.

Effectively captures patch semantics and attributes.

Abstract

Automated program repair (APR) aims to automatically repair program errors without human intervention, and recent years have witnessed a growing interest on this research topic. While much progress has been made and techniques originating from different disciplines have been proposed, APR techniques generally suffer from the patch overfitting issue, i.e., the generated patches are not genuinely correct despite they pass the employed tests. To alleviate this issue, many research efforts have been devoted for automated patch correctness assessment (APCA). In particular, with the emergence of large language model (LLM) technology, researchers have employed LLM to assess the patch correctness and have obtained the state-of-the-art performance. The literature on APCA has demonstrated the importance of capturing patch semantic and explicitly considering certain code attributes in predicting patch correctness. However, existing LLM-based methods typically treat code as token sequences and ignore the inherent formal structure for code, making it difficult to capture the deep patch semantics. Moreover, these LLM-based methods also do not explicitly account for enough code attributes. To overcome these drawbacks, we in this paper design a novel patch graph representation named attributed patch semantic graph (APSG), which adequately captures the patch semantic and explicitly reflects important patch attributes. To effectively use graph information in APSG, we accordingly propose a new parameter-efficient fine-tuning (PEFT) method of LLMs named Graph-LoRA. Extensive evaluations have been conducted to evaluate our method, and the results show that compared to the state-of-the-art methods, our method improves the accuracy and F1 score by 3.1\% to 7.5\% and 3.0\% to 7.1\% respectively.