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The Real‑Vul dataset was developed by the School of Computer Science at the University of Waterloo to provide a comprehensive dataset for evaluating deep‑learning models in real‑world software vulnerability detection. It contains 5,528 C/C++ function samples drawn from diverse software projects such as the Chromium browser and the Linux operating system. The dataset was created using a time‑based split strategy to ensure realistic and timely training and testing data. Real‑Vul is primarily intended for assessing and improving the practical performance of existing vulnerability detection models, especially in complex and varied real‑world software environments.
