The advances of deep learning (DL) have paved the way for automatic software vulnerability repair approaches, which effectively learn the mapping from the vulnerable code to the fixed code. Nevertheless, existing DL-based vulnerability repair methods face notable limitations: 1) they struggle to handle lengthy vulnerable code, 2) they treat code as natural language texts, neglecting its inherent structure, and 3) they do not tap into the valuable expert knowledge present in the expert system. To address this, we propose VulMaster, a Transformer-based neural network model that excels at generating vulnerability repairs by comprehensively understanding the entire vulnerable code, irrespective of its length. This model also integrates diverse information, encompassing vulnerable code structures and expert knowledge from the CWE system. We evaluated VulMaster on a real-world C/C++ vulnerability repair dataset comprising 1,754 projects with 5,800 vulnerable functions. The experimental results demonstrated that VulMaster exhibits substantial improvements compared to the learning-based state-of-the-art vulnerability repair approach. Specifically, VulMaster improves the EM, BLEU, and CodeBLEU scores from 10.2\% to 20.0\%, 21.3\% to 29.3\%, and 32.5\% to 40.9\%, respectively.

翻译：深度学习的进展为自动软件漏洞修复方法铺平了道路，这些方法能有效学习从脆弱代码到修复代码的映射。然而，现有基于深度学习的漏洞修复方法存在显著局限性：1）难以处理冗长的脆弱代码，2）将代码视为自然语言文本而忽略其固有结构，3）未能利用专家系统中宝贵的专家知识。为解决此问题，我们提出了VulMaster —— 一种基于Transformer的神经网络模型，无论代码长度如何，均能通过全面理解整个脆弱代码而擅长生成漏洞修复方案。该模型还整合了多样化信息，涵盖脆弱代码结构以及来自CWE系统的专家知识。我们在一个包含1,754个项目、5,800个脆弱函数的真实C/C++漏洞修复数据集上评估了VulMaster。实验结果表明，与基于学习的最新漏洞修复方法相比，VulMaster展现出显著改进。具体而言，VulMaster将EM、BLEU和CodeBLEU分数分别从10.2%提升至20.0%，从21.3%提升至29.3%，以及从32.5%提升至40.9%。