juxam/C3-VULMAP: C3-VULMAP Dataset v1.0

C3-VULMAP is a large-scale dataset developed to support research in Software Security, Vulnerability Detection, and Machine Learning for secure software development. The dataset contains millions of labeled source code samples mapped to standardized vulnerability categories derived from the Common Weakness Enumeration (CWE) taxonomy. The dataset includes 7,910,174 code samples annotated with vulnerability-related metadata and classification labels. Each entry contains a source code snippet together with its associated vulnerability identifier, vulnerability category, and additional descriptive attributes. The dataset is structured to enable both binary vulnerability detection and multi-class vulnerability classification tasks. C3-VULMAP was designed to facilitate research in automated vulnerability detection using both traditional static analysis techniques and modern machine learning approaches. In particular, the dataset supports experiments involving structural code representations derived from program analysis methods such as the Abstract Syntax Tree (AST) and graph-based learning techniques. The dataset contains 102,038 vulnerable samples (1.3%) and 7,808,136 non-vulnerable samples (98.7%), reflecting the class imbalance typically observed in real-world software vulnerability datasets. The dataset also includes 775 distinct CWE identifiers, enabling detailed studies of vulnerability types and categories across a large corpus of code. To support efficient large-scale data analysis and machine learning pipelines, C3-VULMAP is distributed in both CSV and Parquet formats. The Parquet version enables high-performance data processing for large-scale experiments, while the CSV format ensures accessibility for a wide range of data analysis tools. C3-VULMAP aims to facilitate reproducible research and benchmarking of automated vulnerability detection techniques by providing a large and diverse dataset for evaluating machine learning models and program analysis methods. Dataset Characteristics Property Value Total Samples 7,910,174 Vulnerable Samples 102,038 Non-Vulnerable Samples 7,808,136 Unique CWE IDs 775 Number of Attributes 9 Unique Code Samples 7,663,589 Dataset Formats Parquet (~16 GB)