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45 results for “smart contracts”
A Context-Driven Approach for Co-Auditing Smart Contracts with The Support of GPT-4
<p>This is the latest version that contains all the data related to the experiment. This version is a merge of all the previous versions and does not add any new data</p>
# Artifact: Smart Contract and DeFi Security Tools: Do They Meet the Needs of Practitioners?
<p>This artifact complements the ICSE'24 paper titled "Smart Contract and DeFi Security Tools: Do They Meet the Needs of Practitioners?" by providing comprehensive resources for reproducing and extending its research findings. The artifact includes 'retro.db', a database of 127 DeFi attacks discussed in sections 4.1 and 4.2 of the paper. This database comprises data on the attacks and the outcomes derived from various analysis tools applied to the vulnerable contracts. Additionally, it provides thorough instructions on how to reapply these tools to the dataset, as well as guidance on integrating and running additional tools. Finally, it includes anonymized responses from the survey participants, which are used to recreate the analysis presented in sections 4.3, 4.4, and 4.5. </p>
Additional Resources for End-user Comprehension of Transfer Risks in Smart Contracts
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Empirical Review of Automated Analysis Tools on 47,587 Ethereum Smart Contracts
<p>This dataset contains the full output of the execution of 9 state-of-the-art automated analysis tools on 47,518 Solidity contracts.</p> <p>The data structure is as follows</p> <pre><code>├─ results │ └─ <tool_name> │ └─ <dataset_name> │ └─ <contract_address> │ ├─ <result.log> # stdout of the analysis │ └─ <result.json> # parsable output analysis</code></pre> <p> </p>
Sociotechnical Dynamics in Open Source Smart Contract Repositories: An Exploratory Data Analysis of Curated High Market Value Projects
<p>This is the replication package for the paper “Sociotechnical Dynamics in Open Source Smart Contract Repositories: An Exploratory Data Analysis of Curated High Market Value Projects”.</p> <p>In project_curation_selection, there is the curation process of the 100 selected projects including the identification of GitHub repositories and classification of evolution scenarios. </p> <p>In distribution_commits_issues_contributors_market_value_before_after_deploy, data collection from GitHub projects includes the distribution of total commits, contributors, and issues before and after deployment of each investigated project. </p> <p>In analysis_commit_messages, there is qualitative analysis of commit message content from all investigated projects. </p> <p>In the analysis_contributors section, the data focuses on analyzing the profiles of each GitHub contributor involved in the investigated projects.</p> <p>In analysis_market_value_by_project, data refers to the market value and volume of each investigated project. </p> <p>In codes, there are scripts used to obtain the analyzed data.</p> <p> </p>
Artifacts of the paper "DogeFuzz: A Simple Yet Efficient Grey-box Fuzzer for Smart Contracts."
<div> <h1>Artifacts of the paper "DogeFuzz: A Simple Yet Efficient Grey-box Fuzzer for Smart Contracts."</h1> <a href="https://github.com/faustocarva/dogefuzzsbes_artifact/tree/main#artifacts-of-the-paper-dogefuzz-a-simple-yet-efficient-grey-box-fuzzer-for-smart-contracts"></a></div> <p>This repository contains artifacts for the experiments in the paper DogeFuzz: A Simple Yet Efficient Grey-box Fuzzer for Smart Contracts. The <code>dataset</code> directory includes 2 benchmarks: Bench72 and Bench500. The other directories are <code>dogefuzz-results</code> for processing Dogefuzz-related result files and <code>Smartian-Artifact</code> for processing Smartian-related replication studies.</p>
Understanding Transaction-Reverting Faults in Smart Contracts
<div> <h1>Understanding Transaction-Reverting Faults in Smart Contracts</h1> </div> <p>This project aims to provide a benchmark for smart contract developers and researchers to enhance their understanding of transaction-reverting faults (TR faults) in smart contracts. The project is associated with a paper titled <strong>Understanding Transaction-Reverting Faults in Smart Contracts</strong>.</p> <div> <h2>Dataset Description</h2> </div> <div> <h3>Overview</h3> </div> <p>We identify 301 real-world TR faults from open-source GitHub project and categorize them into machine auditable and machine unauditable faults. Among these faults, 224 (74.4%) fall into the machine auditable category, while the remaining 77 (25.6%) fall into the machine unauditable category. For detailed information on these 301 TR fault contracts, please refer to the <a href="../api/records/11889080/draft/files/TRFaults.zip/content" target="_blank" rel="noopener noreferrer">TRFaults.zip</a>. For more details, please refer to our GitHub repo. </p> <div> <h3>Folder Structure</h3> </div> <p>The dataset is structured into four distinct sections within the <a href="../api/records/11889080/draft/files/TRFaults.zip/content" target="_blank" rel="noopener noreferrer">TRFaults.zip</a> :</p> <ul> <li>machine_auditable_faults: includes the buggy and patched version of the 224 machine auditable faults.</li> <li>machine_unauditable_faults: includes the buggy and patched version of the 77 machine unauditable faults.</li> <li>machine_auditable_faults.csv: contains detailed information of each machine auditable fault, including the commit URL, category, and project type.</li> <li>machine_unauditable_faults.csv: contains the detailed information of each machine unauditable fault, including the commit URL, category, and project type.</li> </ul>
Supplementary material for Evaluating Legal Compliance of Smart Contracts Generated by Large Language Models
<p>This repository contains the supplementary material for the paper titled "Evaluating Legal Compliance of Smart Contracts Generated by Large Language Models". It includes natural-language legal contracts, their smart contract implementations, and Petri net models of said legal contracts contracts.</p>
An Empirical Evaluation of the Effectiveness of Smart Contract Verification Tools - Supplemental Material
<p>An Empirical Evaluation of the Effectiveness of Smart Contract Verification Tools - Supplemental Material</p>
Smart Contracts Versions Sample
<p>We have randomly sampled 100 upgradeable contracts and traced their upgrades and versions. From 100 contracts, 32 contracts were upgraded, resulting in 76 overall versions. </p>
Vulnerability Detection for Smart Contracts: A Systematic Literature Review - Supplementary Material
<p>Supplementary Material refers to the paper "Vulnerability Detection for Smart Contracts: A Systematic Literature Review" and contains the following information:</p> <p>a) DataAnalysis.xls - An Excel file created for analysis (i.e., tables and graphics).</p> <p> </p>
A Systematic Review on Smart Contracts Security Design Patterns – Supplementary Material
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A Context-Driven Approach for Co-Auditing Smart Contracts with The Support of GPT-4
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Ethereum Transaction Data used for End-user Comprehension of Transfer Risks in Smart Contracts
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OpenSCV: An Open Hierachical Taxonomy for Smart Contract Vulnerabilities – Supplemental Material
<p>Dataset and other supplemental material.</p>
PrAIoritize: Automated Early Prediction and Prioritization of Vulnerabilities in Smart Contracts
<p>PrAIoritize: Automated Early Prediction and Prioritization of Vulnerabilities in Smart Contracts</p>
Deep Learning-Based Methodology for Vulnerability Detection in Smart Contracts
<p>智能合约漏洞检测公共数据集</p>
Smart Contracts Corpus Repository: Source Code and Metrics.
<p>Many empirical software engineering studies show that there is a great need for repositories where code is acquired, filtered and classified.<br> During the last few years, Ethereum block explorer services have emerged as a popular project to explore and search Ethereum blockchain data such as transactions, addresses, tokens, smart-contracts' source code, prices and other activities taking place on Ethereum blockchain.<br> Despite the availability of this kind of services, retrieving specific information useful to empirical software engineering studies, such as the study of smart-contracts' software metrics, might be a tedious task that requires different sub-tasks such as searching specific transactions in a block, parsing files in HTML format and filtering the smart-contracts to remove duplicated code or unused smart-contracts.<br> Smac-Corpus aims to create a smart-contracts' repository where smart contracts data (source code, ABI and byte code) are freely and immediately available and also classified based on software metrics identified in the scientific literature.</p>
Deep Learning-Based Methodology for Vulnerability Detection in Smart Contracts
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AutoMESC: Automatic Framework for Mining and Classifying Ethereum Smart Contract Vulnerabilities and Their Fixes
<p>AutoMESC Dataset: Automatic Framework for Mining and Classifying Ethereum Smart Contract Vulnerabilities and Their<br> Fixes<br> <br> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.