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New ARVO Dataset Enhances Open-Source Software Vulnerability Reproducibility

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ARVO, a new dataset designed to improve the reproducibility of vulnerability data in open-source software. This dataset addresses the common trade-off between reproducibility, quantity, and diversity in vulnerability datasets by focusing on making each vulnerability consistently rebuildable, triggerable, and analyzable across different versions. ARVO contains over 6,100 real-world vulnerabilities from 311 projects, successfully reproducing 81% of them and achieving 89.4% accuracy in locating corresponding patches. AI

RANK_REASON The cluster describes a new academic dataset focused on improving reproducibility in software vulnerability research. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New ARVO Dataset Enhances Open-Source Software Vulnerability Reproducibility

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The cluster describes a new academic dataset focused on improving reproducibility in software vulnerability research. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xiang Mei, Jordi Del Castillo, Pulkit Singh Singaria, Haoran Xi, Abdelouahab Benchikh, Tiffany Bao, Ruoyu Wang, Yan Shoshitaishvili, Adam Doup\'e, Hammond Pearce, Brendan Dolan-Gavitt ·

    ARVO: Atlas of Reproducible Vulnerabilities for Open-Source Software

    arXiv:2606.17283v1 Announce Type: cross Abstract: Achieving reproducibility, quantity, and diversity in vulnerability datasets has long been viewed as an inherent three-way trade-off, where improving one dimension often comes at the cost of the others. In practice, reproducibilit…

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