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677 results for “Replication package”
The Impact of the COVID-19 Pandemic on Women's Contribution to Public Code - Replication Package
<p>Replication package for the article "The Impact of the COVID-19 Pandemic on Women's Contribution to Public Code"</p>
Replication Package for: "The Impact of Cartel Dissolution on Prices: Evidence from the Air Cargo Cartel"
<div> <div> <div> <p>This replication package contains an online appendix, data, and code used to generate 4 figures and 2 tables in the main text of the paper "The Impact of Cartel Dissolution on Prices: Evidence from the Air Cargo Cartel" by Douglas C. Turner.</p> </div> </div> </div>
Replication Package for "On the impact of security vulnerabilities in the npm package dependency network
<p>This is the replication package for paper "On the impact of security vulnerabilities in the npm package dependency network" accepted for publication in MSR 2018.</p>
Replication Package: Developer Reading Behavior while Summarizing Java Methods: Size and Context Matters
<p>A replication package for the study presented in the ICSE 2019 paper titled "Developer Reading Behavior while Summarizing Java Methods: Size and Context Matters" by Abid, Sharif, Dragan, Alrasheed, and Maletic</p>
Replication Package for "Automated Reporting of Anti-Patterns and Decay in Continuous Integration"
<p>This is the replication package for the paper "Automated Reporting of Anti-Patterns and Decay in Continuous Integration" accepted for publication at <a href="https://2019.icse-conferences.org/track/icse-2019-Technical-Papers#event-overview">ICSE 2019 (Technical Track)</a>.</p> <p>We include all the artifacts necessary to replicate the results obtained in our paper. Specifically, we provide (i) all the scripts used to conduct our statistical tests and to process the data obtained from our surveys, (ii) the queries used to perform the project selection, (iii) and a runnable version of our CI anti-patterns detection tool along with the external source code used to quantify the presence of CI anti-patterns in our dataset.<br> Furthermore, we provide raw and processed data from our surveys and data (build logs and repositories) that can be used as input to our detection pipeline. We also include a Docker container image with a working environment containing the artifacts.</p> <p>Preprint of corresponding paper is available <a href="http://doi.org/10.5281/zenodo.2578271">here</a>.</p>
Replication package for the paper: We Need to Talk About Microservices
<p>This is a replication package for the paper "We Need to Talk About Microservices", published at the mining challenge track of MSR'19.</p> <p> </p> <p>This package is composed of two files: data.zip and code.zip. On data.zip, one will find the raw and intermediate data for each and every step of our experimental methodology. On code.zip, one will find the source code we employed to perform our analysis.</p>
Replication Package for "How Developers Engage with Static Analysis Tools in Different Contexts"
<p>This is the replication package for the paper "How Developers Engage with Static Analysis Tools in Different Contexts".</p> <p>We include all the artifacts necessary to replicate the results obtained in our paper. Specifically, we provide (i) the survey questions together with all the valid answers we received including the demographics of our respondents, (ii) the most relevant statements that we extracted from the interviews including the demographics of our interviewees, (iii) the results of the card sorting performed on the development activities where our participants adopt Static Analysis Tools, (iv) all the data related to Krippendorff’s Alpha calculation for the performed card sorting, and (v) mapping of ASATs to the "rules" categories defined by Novak et al. (2010) and script for calculating occurrence, definition, and enforcement of the different ASAT types together with input and output data. Furthermore, we include the list of links to Reddit posts and inspected open-source projects together with their inspection data and the scripts for computing the inter-rater agreement during the inspection. Finally, we provide the Github features computed for each project and script for generating the sets of projects.</p>
Replication Package: Reducing the Maintenance Effort for Parameterization of Representative Load Tests Using Annotations
<p>This is the replication package for the publication <em>Reducing the Maintenance Effort for Parameterization of Representative Load Tests Using Annotations</em>, Journal of Software Testing, Verification and Reliability (STVR), Special Issue on Testing Extra-Functional Properties. It contains the JSON/YAML schema of the Input Data and Properties Annotation (IDPA) as well as the experiment artifacts, the experiment results, and the R-scripts we used for the analysis.</p> <p>The README.md (or README.pdf) contains further descriptions and instructions.</p>
Replication Package for MORCoRA: Multi-Objective Refactoring Recommendation Considering Review Availability
<div> <div>This is the replication package for <em>MORCoRA: Multi-Objective Refactoring Recommendation Considering Review Availability</em></div> <br><br> <div> <p><strong>Refactoring sequences searched by MORCoRA</strong></p> </div> <div>The 6 directories contain the refactoring sequences searched by MORCoRA.</div> <div>The name of each directory is the name of the repository introduced in <em>Table 2. Dataset</em>.</div> <br> <div>Each directory includes 6 CSV files.</div> <div>The name of the CSV file represents the search algorithm used to search refactoring sequences.</div> <br> <div>Note that <em>NsgaiiN</em> represents using the NSGA-II algorithm without considering the review availability objective, which is the RA- in <em>Section 4.5</em>.</div> <br> <div>In each CSV file, a single row is the refactoring sequences searched and the recommended reviewers for it. The sequence with no appropriate reviewers will be noted as <em>No appropriate expertise reviewer</em>.</div> <br><br> <div>In each CSV file, a row consists of multiple columns, the last column represents the recommended reviewer, and the rest columns represent elements in the refactoring sequences.</div> <div>Each element consists of:</div> <div> <ul> <li>ROType: refactoring operation type</li> </ul> </div> <div> <ul> <li>class1info: <em>class1</em> in the <em>Table 1. Refactoring Operations</em> in the paper. It is the information of the source class where the number before <em>#</em> represents the access modifiers according to <a href="https://docs.oracle.com/javase/7/docs/api/constant-values.html#java.lang.reflect.Modifier.PROTECTED">modifiers and their corresponding int values</a>, the name after <em>#</em> represents the name for the class</li> </ul> </div> <div> <ul> <li>class1path: the path to the file containing the class</li> </ul> </div> <div> <ul> <li>class2info: <em>class2</em> in the <em>Table 1. Refactoring Operations</em> the formation of the target class</li> </ul> </div> <div> <ul> <li>class2path: the path to the file containing the class</li> </ul> </div> <div> <ul> <li>target: it can be "class" or "method" or "field" according to the refactoring type. The number before <em>#</em> represents the access modifier. The name after <em>#</em> is the name of the "class" or "method" or "field", and its type is revealed after the <em>@</em> symbol.</li> </ul> </div> <br><br> <div><strong>Manually review results</strong></div> <div>The manual review results of the 60 solutions introduced in <em>Section 4.3</em> is recorded in the <em>manually_review_60_solutions.csv</em></div> <br> <div>It includes 6 columns:</div> <div> <ul> <li>Repository: The name of the repository.</li> </ul> </div> <div> <ul> <li>Recommended Refactoring Operations: the searched refactoring sequence.</li> </ul> </div> <div> <ul> <li>Recommended Reviewer: the reviewer recommended to review the refactoring sequence.</li> </ul> </div> <div> <ul> <li>Reviewable: If no appropriate expertise reviewer is found for the sequence, the value is "0", otherwise "1".</li> </ul> </div> <div> <ul> <li>Code smell Eliminated: The code smell type detected by JDeodorant that the recommended refactoring can eliminate. If the refactoring sequence cannot eliminate any code smell, then it is "No".</li> </ul> </div> <div> <ul> <li>Valid: If the refactoring sequence is recommended with appropriate reviewer (value in the column "Reviewable" is "1") and meaningful, and eliminates at least one code smell, the value is "1", otherwise "0".</li> </ul> </div> </div>
Replication package for 'Medically assisted reproduction and non-normative family forms: legislation and public opinion in Europe'
<p>Replication package for the paper "Medically assisted reproduction and non-normative family forms: legislation and public opinion in Europe", accepted for publication in <em>European Societies</em> (2024). </p> <p>This repository provides the R code to replicate the results. It utilizes data from the European Values Study (available at: https://europeanvaluesstudy.eu/) and an original database on the timing of MAR access legislation for single women and same-sex female couples in Europe. </p> <p> </p> <p> </p>
Does Location Influence Code Quality? Mining Stack Overflow Snippets Across the United States – Replication Package
<p>Developers routinely integrate Stack Overflow code snippets into their codebases. However, the quality of snippets embedded in users’ answers remain elusive, and existing evaluations of code quality tend to be language or context-specific. Moreover, literature have found that contribution patterns vary depending on geographical locales, creating an unexplained rift between code quality, user location, and latent contextual regional factors. </p> <p>The proposed study evaluates the quality of SQL, JavaScript, Python, Ruby, and Java snippets across reliability, readability, performance, and security dimensions, benchmarking findings across states in the USA and investigating how different diversity indicators correlate against code quality violations. The study culminates in a series of inductive content analyses that qualitatively supplement prior quality dimensions.</p> <p>This replication package is provided for those interested in further examining our research methodology.</p>
LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models - Replication Package
<p>LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models</p> <p>This is the replication package associated with the paper "LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models".</p> <p><strong>Replication Package Contents:</strong></p> <p>This replication package contains all the necessary data and code required to reproduce the results reported in the paper. We provide the results of the Fault Detection Rate (FDR), Total Minimization Time (MT), Time Saving Rate (TSR) , statistical tests for all the minimization budgets (i.e., 25%, 50%, and 75%), results for the preliminary study, results for UniXcoder/Cosine with preprocessed code on 16 projects.</p> <p><strong>Data:</strong></p> <p>We provide in the <em><strong>Data</strong></em> directory the data used in our experiments, which is the source code of test cases (Java test methods) of 17 projects collected from Defects4J.</p> <p><strong>Code:</strong></p> <p>We provide in the<em> <strong>Code</strong> </em>directory the code (Python) and bash files required to run the experiments and reproduce the results.</p> <p><strong>Results:</strong></p> <p>We provide in the<em> <strong>Results </strong></em>directory the detailed results for our approach (called LTM). We also provide the summarized results of LTM and a baseline (ATM) for comparison purposes. Additional technical details about ATM can be found at https://zenodo.org/record/7455766.</p> <p><strong>_________________________________</strong></p> <p><strong>LTM's Similarity Measurement:</strong></p> <p>The source code of this step is in the <strong><em>Code/LTM/Similarity</em></strong> directory.</p> <p><strong>Requirements:</strong></p> <p>To run this step, Python 3 is required (we used Python 3.10). Also, the required libraries in the <em><strong>Code/LTM/Similarity/requirements.txt</strong></em> file should be installed, as follows:</p> <p>cd Code/LTM/Similarity</p> <p>pip install -r requirements.txt</p> <p><strong>Input:</strong></p> <ul> <li>Data/LTM/TestMethods</li> </ul> <p><strong>Output:</strong></p> <ul> <li>Data/LTM/similarity_measurements</li> </ul> <p><strong>Running the experiment:</strong></p> <p>To measure the similarity between all pairs of test cases, the following bash script should be executed:</p> <p>bash measure_similarity.sh</p> <p>The source code of test methods of each project in the <strong><em>Data/LTM/TestMethods</em></strong> is parsed to generate pairs of test cases. This steps includes test methods tokenization, test methods embeddings extraction and similarity calculation. Then, all similarity scores are stored in <em><strong>Data/LTM/similarity_measurements</strong></em> folder. Due to the large size of the calculated similarity scores (60 GB), they were not uploaded on Zenodo, but they can be available upon request.</p> <p><strong>LTM's Test Suite Minimization:</strong></p> <p>The source code of this step is in the Code/LTM/Search directory.</p> <p><strong>Requirements:</strong></p> <p>To run this step, Python 3 is required (we used Python 3.10). Also, the required libraries in the <strong><em>Code/LTM/Search/requirements.txt</em></strong> file should be installed, as follows:</p> <p>cd Code/LTM/Search</p> <p>pip install -r requirements.txt</p> <p><strong>Input:</strong></p> <p>Data/LTM/similarity_measurements</p> <p><strong>Output:</strong></p> <p>Results/LTM/minimization_results</p> <p><strong>Running the experiments:</strong></p> <p>To minimize the test suite for each project version, the following bash script should be executed:</p> <p>bash minimize.sh</p> <p>The similarity scores of all test case pairs per project version are parsed by the search algorithm (Genetic Algorithm). Each experiment runs ten times using three minimization budgets (25%, 50%, and 75%). The results are stored in the <em><strong>Results/LTM/minimization_results</strong></em> directory.</p> <p><strong>LTM's Evaluation:</strong></p> <p>To evaluate the minimization results for each version and each project, the following bash script should be executed:</p> <p>cd Code/LTM/Evaluation</p> <p>bash evaluate_per_version.sh</p> <p>cd Code/LTM/Evaluation</p> <p>bash evaluate_per_project.sh</p> <p>This will evaluate the FDR, MT and TSR results for each version and each project for each minimization budget. These results are stored in the <em><strong>Results/LTM</strong></em> directory.</p> <p>Note that for each version, the FDR is either 1 or 0. For each project, the FDR ranges from 0 to 1.</p>
Delta GUI change detection using inferred models replication package
<p><strong>TESTAR </strong>is a scriptless automated open-source tool developed by the Universitat Politècnica de València and the Open University of the Netherlands.</p> <p><strong>TESTAR Change detection .NET</strong> is an open-source tool that simultaneously transits and compares state models inferred by a scriptless testing tool, enabling the detection and highlighting of GUI changes to detect the widgets or functionalities that have been added, removed, or modified. This tool is also developed by the Universitat Politècnica de València and the Open University of the Netherlands.</p> <p>This replication package contains:</p> <ul> <li>The Excel file that was used to perform and store the systematic mapping of the literature.</li> <li>The TESTAR Change detection .NET version that was used to compare the state models inferred from the OBS, Calibre, and MyExpenses applications. </li> <li>The state models that were inferred from the OBS, Calibre, and MyExpenses applications. These are stored in the OrientDB graph database. </li> <li>Three documents (OBS, Calibre, MyExpenses) that detail with images the GUI changes results detected using the TESTAR Change detection .NET tool.</li> <li>A video demo that shows how to use the TESTAR Change detection .NET tool with the inferred models from the Calibre web system.</li> </ul>
Replication package and Online Appendix for: Why Do Some New Products Fail? Evidence from the Entry and Exit of Vanilla Coke
<p>This repository contains the replication package of data and codes to generate the main results reported in "Why Do Some New Products Fail? Evidence from the Entry and Exit of Vanilla Coke" by Robert Clark and Yiran Gong, to be published in <em>International Journal of Industrial Organization</em>, and the online appendix of the paper.</p>
Replication package for "Wealth Taxation and Household Saving: Evidence from Assessment Discontinuities in Norway"
<p>This package contains replication files and instructions for "Wealth Taxation and Household Saving: Evidence from Assessment Discontinuities in Norway" by Marius Ring to be published in the <em>Review of Economic Studies</em>.</p>
Replication package for "On the family origins of human capital formation: Evidence from donor children"
<p>The package contains the code necessary to reproduce the tables in Lundborg, Plug, Wurtz-Rasmussen (forthcoming) "On the family origins of human capital formation: Evidence from donor children", Review of Economic Studies. Detailed instructions are also given about accessing the raw data.</p>
A Model-Driven, Metrics-Based Approach to Assessing Support for Quality Aspects in MLOps System Architectures: Replication Package
<div> <p><strong>Title:</strong> A Model-Driven, Metrics-Based Approach to Assessing Support for Quality Aspects in MLOps System Architectures: Replication Package</p> <p><strong>Authors:</strong> Stephen John Warnett; Uwe Zdun</p> <p><strong>About:</strong> This is the replication package artefact for the paper entitled "A Model-Driven, Metrics-Based Approach to Assessing Support for Quality Aspects in MLOps System Architectures".</p> <p><strong>Paper Abstract:</strong> In machine learning (ML) and machine learning operations (MLOps), automation serves as a fundamental pillar, streamlining the deployment of ML models and representing an architectural quality aspect. Support for automation is especially relevant when dealing with ML deployments characterised by the continuous delivery of ML models. Taking automation in MLOps systems as an example, we present novel metrics that offer reliable insights into support for this vital quality attribute, validated by ordinal regression analysis. Our method introduces novel, technology-agnostic metrics aligned with typical Architectural Design Decisions (ADDs) for automation in MLOps. Through systematic processes, we demonstrate the feasibility of our approach in evaluating automation-related ADDs and decision options. Our approach can itself be automated within continuous integration/continuous delivery pipelines. It can also be modified and extended to evaluate any relevant architectural quality aspects, thereby assisting in enhancing compliance with non-functional requirements and streamlining development, quality assurance and release cycles.</p> </div>
Replication package for: Acceptance of inequality between children: Large-scale experimental evidence from China and Norway
<p>CAppelen, Alexander W., Falch, Ranveig, Huang, Zhongjing, and Tungodden, Bertil, "Acceptance of inequality between children: Large-scale experimental evidence from China and Norway," October 2024</p>
Replication package for: "Atlantic Trade and Conflict in Europe"
<p>Replication package for Reshad N. Ahsan, Laura Panza, and Yong Song "Atlantic Trade and Conflict in Europe", published in the Economic Journal<br><br></p>
Replication Package for "Ensuring Open Source Integrity: The Intersection of Copy-Based Reuse and License Compliance"
<p>Replication Package for "Ensuring Open Source Integrity: The Intersection of Copy-Based Reuse and License Compliance"<br><br>Includes datasets, R and bash code.</p>
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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.