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6 results for “Code contracts”
Dataset: A Fly in the Ointment: An Empirical Study on the Characteristics of Ethereum Smart Contracts Code Weaknesses and Vulnerabilities
<p>Dataset: A Fly in the Ointment: An Empirical Study on the Characteristics of Ethereum Smart Contracts Code Weaknesses and Vulnerabilities</p> <p>Majd Soud, Grischa Liebel, Mohammad Hamdaqa<br> majd18@ru.is, grischal@ru.is, mhamdaqa@polymtl.ca.</p> <p>This Dataset includes the following: </p> <p>1. "labeling.xml" files that represents the data for Categories of vulnerabilities in Smart Contracts for four data sources (i.e., Common Vulnerability and Exposure (CVE), Smart Contract Weakness Classification Registry (SWC), Stack Overflow, and GitHub)<br> XML files structure:<br> The XML files can be opened used any editor or any code editor (e.g. Visual Studio Code).</p> <p>1. Each file has a root that is <Card_Table></Card_Table> which contains all the cards we labeled. <br> 2. Each card is represented by the <Card></Card> and contains the following:<br> - The tag marked by <Tag> represents the keyword that was used to search and collect the card from StackOverflow. <br> - The URL marked by <URL> of the URL link which contains all the information of the labeled vulnerability. <br> - The other tages marked by <OtherTags> that shows all the tags used in the post on Stack Overflow. <br> - The expert labeling for the categories of vulnerabilities in each card is represented by <CategoryExpertLabel><br> - In more details, some records has the <SecondExpertCategoryLabel> that represents the second expert labeling for the categories of vulnerabilities.<br> - The <CategoryAgreement> used to calculate the inter-rater agreement between the two labelers. <br> </p>
SmartEmbed Code Clone Analysis of 200 000 Smart Contracts
<p>A dataset consisting of the code clone analysis for 200 000 Ethereum Smart Contracts via the code clone detection tool <a href="https://github.com/beyondacm/SmartEmbed">SmartEmbed</a> (state as is at commit 32ec1cd) . Each Smart Contract has been tested for a similarity threshold of 95% similarity and a threshold of 100% similarity. </p> <p>The result folder offers 255 CSV files, all containing the colums: counter,id of the contract, result for 95%, result for 100%.<br> Possible results for the colum 'result for 95%' are '95' (identified as a code clone with at least 95% similarity), '0' (not a code clone) and null (SmartEmbed returned an error). Similarily for the colum 'result for 100%'.</p> <p>The folder 'Additional_output_per_batch' contains additional data for each batch in .txt files (runtime, ratio of clones to non clones, number of errounous responses, id's of the contracts that could not be analyzed).</p>
Automated Generation of Code Contracts - Generative AI to the Rescue?
<p>This replication package provides the setup and results to generate OpenJML code contracts for Java source code by fine-tuning and employing the resulting CodeT5 and CodeT5+ transformer models. Our code contract generation setup involved the training of the AI models and application. Furthermore, we analyzed the generated annotations wrt. thier logical validity and the type of OpenJML compilation errors. Both methods, together with the results are similarly provided.</p> <p><strong>Source Code Repository (see also scripts-sources.tar): </strong></p> <ul> <li><a href="https://github.com/SEG-UNIBE/auto-generated-code-contracts">https://github.com/SEG-UNIBE/auto-generated-code-contracts</a></li> <li><a href="../doi/10.5281/zenodo.13356451">https://zenodo.org/doi/10.5281/zenodo.13356451</a></li> </ul> <p><strong>Replication Package: </strong>contains the following [folders]</p> <ul> <li><strong>Scripts:</strong> <ul> <li>[scripts-sources.tar]: source codes of the following scripts <ul> <li>Python scripts that we used for training and adding the OpenJML code contracts to the Java methods</li> <li>automated analyses of the studied source code classes and the type of compilation errors</li> </ul> </li> </ul> </li> <li><strong>Sourcegraph Search Results:</strong> <ul> <li>[sourcegraph-results.tar]: the results of the Sourcegraph search queries </li> </ul> </li> <li><strong>Datasets:</strong> <ul> <li>[dataset.tar]: the dataset including the weka-project which contributes two-thirds of the contracts</li> <li>[dataset-withoutweka.tar]: the dataset without weka, which is significantly smaller and was used to examine the performance bias when training and testing without weka</li> </ul> </li> <li><strong>CodeT5 Models:</strong><br> <ul> <li>[codet5-contracts.tar]: the best performing CodeT5 model which was fine-tuned to create OpenJML annotations for methods</li> <li>[codet5p-contracts.tar]: the best performing CodeT5+ model which was fine-tuned to create OpenJML annotations for methods</li> <li>[codet5p-contracts-withoutweka.tar]: the CodeT5+ model which was trained without weka on the same task</li> </ul> </li> <li><strong>Analysis Results:</strong> <ul> <li>[analysis-results.tar/compilability-analysis]: the results of the compilability analysis <ul> <li>the subjects to which we applied the best performing CodeT5+</li> <li>the compilation results and their analysis</li> </ul> </li> <li>[analysis-results.tar/logical-analysis] the results of the logical analysis <ul> <li>the analysis of logic validity of SimpleStack and SimpleTicTacToe</li> </ul> </li> </ul> </li> </ul>
Data and code for: Red-listed plants are contracting their elevational range faster than common plants in the European Alps
<p>Dataset of the publication Geppert C., Bertolli A., Prosser F., Marini L., (2023): Red-listed plants are contracting their elevational range faster than common plants in the European Alps. PNAS. Contacts: costanza.geppert@unipd.it - lorenzo.marini@unipd.it.</p> <p>This data repository consists of plant records collected in the Trento Province from 1990 to 2019, species' elevational range shifts, ecological traits, hotspots' files and R script. </p> <p>Description of the dataset: please see ReadMe.docx containing information on each file and instructions for use.</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>
Data and code for 'An invasive pathogen generally contracts species to their niche cores, not margins'
<p>This repository provides all data and R scripts required to recreate the analyses for 'An invasive pathogen generally contracts species to their niche cores, not margins'.</p>
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