<?xml version='1.0' encoding='UTF-8'?><metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns="http://dublincore.org/documents/dcmi-terms/"><dcterms:title>SARMF-Bench: Reproducible Smart Contract Vulnerability Benchmark Dataset</dcterms:title><dcterms:identifier>https://doi.org/10.7910/DVN/0SP3OO</dcterms:identifier><dcterms:creator>Tiwari, Mohit</dcterms:creator><dcterms:publisher>Harvard Dataverse</dcterms:publisher><dcterms:issued>2026-03-08</dcterms:issued><dcterms:modified>2026-03-11T20:58:43Z</dcterms:modified><dcterms:description>Official SARMF-Bench landing page:
https://profmohit-edu.github.io/sarmf-framework/

SARMF-Bench is a compact and reproducible benchmark dataset for smart contract vulnerability analysis.

The dataset contains five Solidity smart contracts (SC01–SC05) representing canonical vulnerability classes aligned with the Smart Contract Weakness Classification (SWC) registry, including:

SC01 – Reentrancy
SC02 – Arithmetic Overflow Behavior
SC03 – Access Control Weakness
SC04 – Unchecked External Call
SC05 – Denial-of-Service Pattern

Each contract is intentionally minimal to isolate structural vulnerability patterns and support controlled benchmarking experiments for static analyzers, symbolic execution engines, fuzzers, and AI-assisted security tools.

The dataset also includes machine-readable analysis outputs generated using Slither v0.11.5, preserving detector identifiers, impact levels, and confidence metadata to facilitate reproducible vulnerability detection experiments.

Primary development repository:
https://github.com/profmohit-edu/sarmf-framework

Software archive DOI:
https://doi.org/10.5281/zenodo.18754015

Reproducibility protocol DOI:
https://doi.org/10.17504/protocols.io.bp216eyxdgqe/v1</dcterms:description><dcterms:subject>Computer and Information Science</dcterms:subject><dcterms:subject>smart contracts, blockchain security, ethereum, static analysis, smart contract vulnerabilities, benchmark dataset, reproducible research</dcterms:subject><dcterms:date>2026-03-08</dcterms:date><dcterms:contributor>Tiwari, Mohit</dcterms:contributor><dcterms:dateSubmitted>2026-03-06</dcterms:dateSubmitted><dcterms:license>CC0 1.0</dcterms:license></metadata>