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Open-Source Software (OSS) vulnerabilities bring great challenges to thesoftware security and pose potential risks to our society. Enormous effortshave been devoted into automated vulnerability detection, among which deeplearning (DL)-based approaches have proven to be the most effective. However,the current labeled data present the following limitations: (1) \textbf{TangledPatches}: Developers may submit code changes unrelated to vulnerability fixeswithin patches, leading to tangled patches. (2) \textbf{LackingInter-procedural Vulnerabilities}: The existing vulnerability datasetstypically contain function-level and file-level vulnerabilities, ignoring therelations between functions, thus rendering the approaches unable to detect theinter-procedural vulnerabilities. (3) \textbf{Outdated Patches}: The existingdatasets usually contain outdated patches, which may bias the model duringtraining.  To address the above limitations, in this paper, we propose an automated datacollection framework and construct the first repository-level high-qualityvulnerability dataset named \textbf{ReposVul}. The proposed framework mainlycontains three modules: (1) A vulnerability untangling module, aiming atdistinguishing vulnerability-fixing related code changes from tangled patches,in which the Large Language Models (LLMs) and static analysis tools are jointlyemployed. (2) A multi-granularity dependency extraction module, aiming atcapturing the inter-procedural call relationships of vulnerabilities, in whichwe construct multiple-granularity information for each vulnerability patch,including repository-level, file-level, function-level, and line-level. (3) Atrace-based filtering module, aiming at filtering the outdated patches, whichleverages the file path trace-based filter and commit time trace-based filterto construct an up-to-date dataset.

A Repository-Level Dataset For Detecting, Classifying and Repairing Software Vulnerabilities