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 \textbf{\textit{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 (\textbf{9M} in total), \textbf{248} high-quality buggy functions, and \textbf{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 \textbf{\textit{Defects4C}}, we conduct a comprehensive empirical study evaluating the effectiveness of \textbf{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.