{"ID":2856143,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.11059","arxiv_id":"2510.11059","title":"Defects4C: Benchmarking Large Language Model Repair Capability with C/C++ Bugs","abstract":"Automated Program Repair (APR) plays a critical role in enhancing the quality and reliability of software systems. While substantial progress has been made in Java-based APR, largely facilitated by benchmarks like Defects4J, there remains a significant gap in research on C/C++ program repair, despite the widespread use of C/C++ and the prevalence of associated vulnerabilities. This gap is primarily due to the lack of high-quality, open-source benchmarks tailored for C/C++. To address this issue, we introduce Defects4C, a comprehensive and executable benchmark specifically designed for C/C++ program repair. Our dataset is constructed from real-world C/C++ repositories and includes a large collection of bug-relevant commits (9M in total), 248 high-quality buggy functions, and 102 vulnerable functions, all paired with test cases for reproduction. These resources enable rigorous evaluation of repair techniques and support the retraining of learning-based approaches for enhanced performance. Using Defects4C, we conduct a comprehensive empirical study evaluating the effectiveness of 24 state-of-the-art large language models (LLMs) in repairing C/C++ faults. Our findings offer valuable insights into the strengths and limitations of current LLM-based APR techniques in this domain, highlighting both the need for more robust methods and the critical role of Defects4C in advancing future research","short_abstract":"Automated Program Repair (APR) plays a critical role in enhancing the quality and reliability of software systems. While substantial progress has been made in Java-based APR, largely facilitated by benchmarks like Defects4J, there remains a significant gap in research on C/C++ program repair, despite the widespread use...","url_abs":"https://arxiv.org/abs/2510.11059","url_pdf":"https://arxiv.org/pdf/2510.11059v2","authors":"[\"Jian Wang\",\"Xiaofei Xie\",\"Qiang Hu\",\"Shangqing Liu\",\"Jiongchi Yu\",\"Jiaolong Kong\",\"Yi Li\"]","published":"2025-10-13T06:49:28Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}