A Benchmark Dataset for Code-Level Vulnerability Detection and Analysis

We present PyCode_Vul, a Python-based software vulnerability dataset constructed from 15 open-source GitHub projects. The corpus comprises 17,811 function-level instances, including 7,899 vulnerable and 9,912 non-vulnerable samples. Our pipeline mines commit histories, extracts code changes, and recovers complete functions with AST-validated parsing. Labels are assigned via CWE mapping that combines heuristic patterns with the Bandit static analysis tool, followed by rigorous deduplication to reduce leakage and near-duplicates. We benchmark ten large language models (LLMs) on PyCode_Vul and evaluate cross-dataset generalization on CVEfixes, VUDENC, PyData, Cod_Vulnerability_Python, Buggy_Python, and PCV+Merge, alongside our PyCode_Vul Test split. Results indicate that UniXcoder and CodeT5+ consistently achieve the best overall performance on our proposed test set and the merged split, indicating that PyCode_Vul exhibits a coherent, learnable distribution for LLM-based vulnerability detection.