"A Dataset for AI generated and human written code Vulnerabilities detection and patch recommendation with explanation."

"The dataset consists of well-organized and curated source code samples that are meant for automated detection, classification, and fixing up of software security vulnerabilities. It has marked occurrences of both AI-generated and human-written Python code showing up with the same security flaws like - SQL Injection, Command Injection, Cross-Site Scripting (XSS), Path Traversal, Hardcoded Secrets, and Unsafe Dynamic Code Execution (eval\/exec).\r\n\r\nThe dataset contains each vulnerability marked by a type, severity level (Low, Medium, High), and an associated upgraded version of the code along with a human-readable explanation that describes the cause and the mitigation measures taken. The dataset is consistent in terms of both the schema used and the vulnerability semantics, which in turn facilitates reproducibility and explainability.\r\n\r\nThis dataset is not like the case of large-scale unlabeled code corpses, rather it is a quality, interpretability, and security relevance focused dataset. Thus it is especially fit for applications in supervised machine learning, vulnerability-aware program analysis, and security education. The dataset will greatly contribute to research in secure code generation, risk assessment of AI-generated code, automated software repair, and explainable security systems."