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677 results for “Replication package”
How have views on Software Quality differed over time? Research and practice viewpoints (Replication package)
<p>Theoretical and practical viewpoints on the quality of code snippets.</p>
Data set for the replication package of the paper "Simulations of DNA-origami self-assembly reveal design-dependent nucleation barriers"
<p>Data set for the replication package of the paper "Simulations of DNA-origami self-assembly reveal design-dependent nucleation barriers".</p>
Flakify: A Black-Box, Language Model-based Predictor for Flaky Tests – Replication Package
<p>This is the replication package associated with the paper: <em>Flakify: A Black-Box, Language Model-based Predictor for Flaky Tests.</em> We explain how to use it to reproduce the results reported in the paper. A maintainable version of this replication package is available on GitHub (<a href="https://github.com/uOttawa-Nanda-Lab/Flakify">https://github.com/uOttawa-Nanda-Lab/Flakify</a>).</p> <p><strong>Flakify Test Smell Detector</strong></p> <p>This is a step-by-step guideline to detect test smells in the source code of test cases and retain statements that match them.</p> <p><em><strong>Requirements:</strong></em></p> <ul> <li>Eclipse IDE (the version we used was 2021-12)</li> <li>The libraries (the <strong><em>.jar</em></strong> files in the <strong><code>lib\</code></strong> directory)</li> </ul> <p><em><strong>Input Files:</strong></em></p> <p>This is a list of input files that are required to accomplish this step:</p> <ul> <li> <p><em>dataset/FlakeFlagger/FlakeFlagger_filtered_dataset.csv</em></p> </li> <li> <p><em>dataset/FlakeFlagger/FlakeFlagger_class_files/</em></p> </li> <li> <p><em>dataset/IDoFT/IDoFT_filtered_dataset.csv</em></p> </li> <li> <p><em>dataset/IDoFT/IDoFT_class_files/</em></p> </li> </ul> <p>The <strong><code>dataset/FlakeFlagger/FlakeFlagger_filtered_dataset.csv</code></strong> and <strong><code>dataset/IDoFT/IDoFT_filtered_dataset.csv</code></strong> are used to obtain the label (<em>flaky</em>=1 or <em>non-flaky</em>=0) and project name for each test case parsed from <strong><code>dataset/FlakeFlagger/FlakeFlagger_class_files/</code></strong> and <strong><code>dataset/IDoFT/IDoFT_class_files/</code></strong>, respectively.</p> <p><strong><em>Output Files:</em></strong></p> <ul> <li> <p><em>dataset/FlakeFlagger/FlakeFlagger_dataset.csv</em></p> </li> <li> <p><em>dataset/FlakeFlagger/FlakeFlagger_test_cases_full_code/</em></p> </li> <li> <p><em>dataset/FlakeFlagger/FlakeFlagger_test_cases_preprocessed_code/</em></p> </li> <li> <p><em>dataset/IDoFT/IDoFT_dataset.csv</em></p> </li> <li> <p><em>dataset/IDoFT/IDoFT_test_cases_full_code/</em></p> </li> <li> <p><em>dataset/IDoFT/IDoFT_test_cases_preprocessed_code/</em></p> </li> </ul> <p> </p> <p><strong>Replicating the experiment</strong></p> <p>To detect test smells and retain only code statements related to them, the <strong><code>src/FlakifySmellsDetector.java</code></strong> file should be compiled and run using the Eclipse IDE by having all the <em>.jar</em> files in the classpath.</p> <p>The pre-generated executable Jar file <strong><code>src/FlakifySmellsDetector.jar</code></strong> can be executed using the shell script <strong><code>src/FlakifySmellsDetector.sh</code></strong> after changing paths for each dataset as needed, using the following commands:</p> <pre><code class="language-bash">bash FlakifySmellsDetector.sh FlakeFlagger bash FlakifySmellsDetector.sh IDoFT</code></pre> <p>It will generate the dataset required to run Flakify's flaky test prediction model for the datasets given as input. The class file containing each of the test cases is then parsed to produce the corresponding full code and pre-processed code of the test case. The full and pre-processed source code of all test cases are also combined and saved in a CSV file, along with test smells found, project names, and labels.</p> <p> </p> <p><strong>Flakify Replication</strong></p> <p>This is the guideline for replicating the experiments we used to evaluate Flakify for classifying test cases as <em>flaky</em> and <em>non-flaky</em> using both cross-validation and per-project validation.</p> <p><em><strong>Requirements:</strong></em></p> <p>This is a list of all required python packages:</p> <ul> <li><em>python =3.8.5</em></li> <li><em>imbalanced_learn= 0.8.1</em></li> <li><em>numpy= 1.19.5</em></li> <li><em>pandas= 1.3.3</em></li> <li><em>transformer= 4.10.2</em></li> <li><em>torch=1.5.0</em></li> <li><em>scikit_learn= 0.22.1</em></li> </ul> <p><em><strong>Input Files:</strong></em></p> <p>This is a list of input files that are required to accomplish this step:</p> <ul> <li><em>dataset/FlakeFlagger/Flakify_FlakeFlagger_dataset.csv</em></li> <li><em>dataset/IDoFT/Flakify_IDoFT_dataset.csv</em></li> </ul> <p>This file contains the full code and pre-processed code of the test cases in both FlakeFlagger and IDOFT datasets, along with their ground truth labels (<em>flaky</em> and <em>non-flaky</em>).</p> <p><em><strong>Output File:</strong></em></p> <ul> <li> <p><em>results/Flakify_cross_validation_results_on_FlakeFlagger_dataset.csv</em></p> </li> <li> <p><em>results/Flakify_per_project_results_on_FlakeFlagger_dataset.csv</em></p> </li> <li> <p><em>results/Flakify_model_trained_on_FlakeFlagger_dataset.pt</em></p> </li> <li> <p><em>results/Flakify_cross_validation_results_on_IDoFT_dataset.csv</em></p> </li> <li> <p><em>results/Flakify_per_project_results_on_IDoFT_dataset.csv</em></p> </li> <li> <p><em>results/Flakify_model_trained_on_IDoFT_dataset.pt</em></p> </li> </ul> <p> </p> <p><strong>Replicating Flakify experiments</strong></p> <p><strong>Cross-Validation</strong></p> <p>To run the Flakify experiment using cross-validation on the two datasets, navigate to <code>src\</code> folder and run the following commands:</p> <pre><code class="language-bash">bash Flakify_predictor_cross_validation.sh FlakeFlagger bash Flakify_predictor_cross_validation.sh IDoFT</code></pre> <p>This will generate the classification results into <strong><code>results/Flakify_cross_validation_results_on_FlakeFlagger_dataset.csv</code></strong> and <strong><code>results/Flakify_cross_validation_results_on_IDoFT_dataset.csv</code></strong> for the cross-validation experiments on both datasets. It will also save the weights of the two models trained on the FlakeFlagger and IDoFT datasets into <strong><code>results/Flakify_model_trained_on_FlakeFlagger_dataset.pt</code></strong> and <code><strong>results/Flakify_model_trained_on_IDoFT_dataset.pt</strong></code>, respectively.</p> <p> </p> <p><strong>Per-project Validation</strong></p> <p>To run the Flakify experiment using per-project validation on the two datasets, navigate to <code>src\</code> folder and run the following commands:</p> <pre><code class="language-bash">bash Flakify_predictor_per_project.sh FlakeFlagger bash Flakify_predictor_per_project.sh IDoFT</code></pre> <p>This will generate the classification results into <strong><code>results/Flakify_per_project_results_on_FlakeFlagger_dataset.csv</code></strong> and <strong><code>results/Flakify_per_project_results_on_IDoFT_dataset.csv</code></strong> for the whole per-project validation experiments on both datasets.</p> <p> </p> <p><strong>FlakeFlagger Replication</strong></p> <p>This is the guideline for replicating the experiments we used to evaluate the two versions of FlakeFlagger, white-box and black-box, for classifying test cases as <em>flaky</em> and <em>non-flaky</em> using cross-validation on the FlakeFlagger dataset.</p> <p><em><strong>Requirements:</strong></em></p> <p>This is a list of all required python packages:</p> <ul> <li><em>python =3.8.5</em></li> <li><em>imbalanced_learn= 0.8.1</em></li> <li><em>pandas= 1.3.3</em></li> <li><em>scikit_learn= 0.22.1</em></li> </ul> <p><em><strong>Input File:</strong></em></p> <p>This is a list of input files that are required to accomplish this step:</p> <ul> <li><em>dataset/FlakeFlagger/FlakeFlagger_filtered_dataset.csv</em></li> <li><em>dataset/FlakeFlagger/FlakeFlaggerFeaturesTypes.csv</em></li> <li><em>dataset/FlakeFlagger/Information_gain_per_feature.csv</em></li> </ul> <p><em><strong>Output File:</strong></em></p> <ul> <li><em>results/FlakeFlagger_black-box_results.csv</em></li> <li><em>results/FlakeFlagger_white-box_results.csv</em></li> </ul> <p> </p> <p><strong>Replicating FlakeFlagger experiments</strong></p> <p>To run the FlakeFlagger experiments, navigate to <code>src\</code> folder and run the following command:</p> <pre><code class="language-bash">bash FlakeFlagger_predictor.sh white-box bash FlakeFlagger_predictor.sh black-box</code></pre> <p>This will generate the classification results into <strong><code>results/FlakeFlagger_white-box_results.csv</code> </strong>and <strong><code>results/FlakeFlagger_black-box_results.csv</code> </strong>for both white-box and black-box experiments, respectively.</p>
Replication Package for ICSE 2023 submission "How Deep Learning Packages Form Supply Chain in PyPI: Types, Clusters, and Detachment"
<p>This is the replication package for our ICSE 2023 submission <em><strong>How Deep Learning Packages Form Supply Chain in PyPI: Types, Clusters, and Detachment</strong></em>. </p>
Women leaving the playpen - Replication package
<p>This package provides the datasets and programme files used to replicate the empirical results in the paper “Women leaving the playpen: The emancipating role of female suffrage” by Michaela Slotwinski and Alois Stutzer.</p>
Replication package for "Pitfalls and Guidelines for Using Time-Based VCS Data"
<p>This dataset includes scripts, text files, and JSON files used to generate all analysis and results from the paper. A README.md file is included for details on using the scripts - though all of the data the scripts generate should already be cached as JSON or txt files and none of the scripts actually need run.</p> <p>It also includes a spreadsheet containing the paper survey results and manual judgements.</p> <p>The scripts are also on GitHub: <a href="https://github.com/psybers/emse21-timestudy">https://github.com/psybers/emse21-timestudy</a></p>
Replication package for: "Auctions with Privately Known Capacities: Understanding Competition among Renewables"
<p>Code and data used to produce the empirical results in Fabra and Llobet (2022) "Auctions with Privately Known Capacities: Understanding Competition among Renewables". The regression results were obtained using <em>Stata</em>. Data has been further processed in <em>R</em>. The equilibrium price offers have been simulated using <em>Julia</em>.</p>
Replication Package for "An Empirical Comparison of Dependency Network Evolution in Seven Software Packaging Ecosystems"
<p>This is the replication package for the article "An Empirical Comparison of Dependency Network Evolution in Seven Software Packaging Ecosystems" published in the Empirical Software Engineering journal.</p> <p>This package requires Python 3.5 and all the dependencies that are listed in "requirements.txt".<br> The notebooks (in "notebooks" folder) should be opened and executed with Jupyter.</p> <p>The notebooks require the graphs (in "graphs" folder) to be computed first. To do so, execute "helpers.py" with Python.<br> The graphs are built using the data provided by https://libraries.io under CC BY-SA<br> https://creativecommons.org/licenses/by-sa/4.0/<br> Those data can be found in the "data" folder.</p> <p> </p>
Replication package for the article 'The residential patterns of Swiss urban elites. Continuity and change across elite categories (1890-2000),' to appear in the journal 'European Societies,' authored by Pierre Benz; Michael A. Strebel; Roberto Di Capua & André Mach.
<p>The documents in this replication package serve to reproduce the analysis for the article <br>'The residential patterns of Swiss urban elites. Continuity and change across elite categories (1890-2000),' to appear in<br>the journal 'European Societies,' authored by Pierre Benz; Michael A. Strebel; Roberto Di Capua & André Mach.<br>This replication package is created by Pierre Benz (pierre.benz@unil.ch).</p>
Replication package for: Search Complementarities, Aggregate Fluctuations, and Fiscal Policy
<p>Fernández-Villaverde J, Mandelman F, Yu Y, Zanetti F. Search complementarities, aggregate fluctuations, and fiscal policy. <em>Review of Economic Studies</em></p>
Replication package for the paper "What Makes Programmers Laugh? Exploring the Submissions of the Subreddit r/ProgrammerHumor.".
<p>Replication package for the paper "What Makes Programmers Laugh? Exploring the Submissions of the Subreddit r/ProgrammerHumor." Accepted to ESEM '24.</p>
Replication package for our paper entitled "Faster and Better Quantum Software Testing through Specification Reduction and Projective Measurements"
<p><br> -Experiment1<br> Contains the source files for experiment 1 and the study subjects.</p> <p> -Experiment2<br> Contains the source files for experiment 2 and a mutation generator.</p> <p> -results<br> Contains the results from experiment 1 and 2 and postprocessing files</p> <p> -RQ1,RQ2,RQ3<br> Contains source files for figures, tables and data for each research question<br> of the paper</p> <p><br>Please inspect the README.txt file for more details.</p>
Replication package of "Higher Fault Detection Through Novel Density Estimators in Unit Test Generation"
<p>Replication package for the paper "Higher Fault Detection Through Novel Density Estimators in Unit Test Generation" accepted at the Symposium on Search-based Software Engineering (SSBSE) 2024.</p> <p> </p> <p> </p>
Replication Package for "On the correlation between Architectural Smells and Static Analysis Warnings"
<div> <div> <div> <div> <div> <h1>Replication Package for the Paper: "On the Relation between Architectural Smells and Static Analysis Warnings"</h1> <h2>Authors</h2> <ul> <li>Matteo Esposito, University of Oulu, Finland</li> <li>Mikel Robredo, University of Oulu, Finland</li> <li>Francesca Arcelli Fontana, University of Milano-Bicocca, Italy</li> <li>Valentina Lenarduzzi, University of Oulu, Finland</li> </ul> <h2>Content Overview</h2> <p>This replication package contains the following materials:</p> <ul> <li><strong>Tables:</strong> Excel files that include all hypothesis testing data, including normality tests for each hypothesis.</li> <li><strong>Data:</strong> RAW Qualitas Corpus dataset and aggregated SAT output.</li> <li><strong>SAT Instructions:</strong> A PDF file providing detailed instructions for the SAT setup and execution.</li> </ul> <h2>Contact Information</h2> <p>For any issues, questions, or further assistance, please do not hesitate to contact the authors of the paper. We are here to help!</p> </div> </div> </div> </div> </div>
Replication Package: The Past, Present, and Future of Research on the Continuous Development of AI
<p>Replication package for the publication regarding the <strong>The Past, Present, and Future of Research on the Continuous Development of AI.</strong></p> <p> </p> <p> </p>
Replication Package for Understanding the Impact of APIs Behavioral Breaking Changes on Client Applications
<p>This repository contains the replication package for the paper Understanding the Impact of APIs Behavioral Breaking Changes on Client Applications.<br>This paper will be published in the Proceedings of the ACM on Software Engineering journal. The replication package includes the scripts and data we extracted, leading us to our findings.</p>
Replication Package: How Artificial-Intelligence Incidents Can Jeopardize Safety and Security
<p><strong>Welcome to the public repository for the additional content of the paper "SoK: How Artificial-Intelligence Incidents Can Jeopardize Safety and Security", accepted at the ARES 2024.</strong></p> <p>This repository provides additional information to the conducted study, including the following file:</p> <ul> <li>analysis_sheet_ARES2024.csv: sheet containing security- and safety-related data regarding the harmonized datasets AIAAIC, AIID, AVID and Where In the World Is AI?, including 240 incidents classified according to the extraction criteria given in the paper.</li> </ul>
Harmonising Contributions: Exploring Diversity in Software Engineering through CQA Mining on Stack Overflow – Replication Package
<p>Community question-and-answering platforms dedicated to software engineering, such as Stack Overflow, have assumed indispensable roles in fostering a thriving global knowledge ecosystem. As these platforms suffer from diversity-related issues, investigating the underlying reasons behind such challenges becomes imperative to devise potential intervention strategies.</p> <p>The proposed study highlights Stack Overflow users’ contribution profiles, both in isolation and relative to various diversity metrics, including GDP and access to electricity. Finally, the study explores whether these contribution profiles extend to the city and state levels.</p> <p>This replication package complements our study, prompting future scholars to further examine our research process or conduct follow up analyses.</p>
Replication Package: Navigating the Complexity of Generative AI Adoption in Software Engineering
<p>This paper explores the adoption of Generative Artificial Intelligence (AI) tools and Large Language Models (LLMs) within the domain of software engineering, focusing on the influencing factors at the individual, technological, and social levels. We applied a convergent mixed-methods approach to offer a comprehensive understanding of AI adoption dynamics. We initially conducted a structured interview study with 100 software engineers, drawing upon the Technology Acceptance Model (TAM), the Diffusion of Innovations theory (DOI), and the Social Cognitive Theory (SCT) as guiding theoretical frameworks. Employing the Gioia Methodology, we derived a preliminary theoretical model of AI adoption in software: the Human-AI Collaboration and Adaptation Framework (HACAF). This model was then validated using Partial Least Squares – Structural Equation Modeling (PLS-SEM) based on data from 183 software professionals. Our research unveils the complex dynamics at play in AI adoption within software engineering. Findings indicate that at this early stage of AI integration, the compatibility of AI tools within existing development workflows predominantly drives their adoption, challenging conventional technology acceptance theories. The impact of perceived usefulness, social factors, and personal innovativeness seems less pronounced than expected. The study provides crucial insights for future AI tool design and offers a framework for developing effective organizational implementation strategies.</p>
Adaptive Parameter Control for Search-Based Unit Test Generation — Replication Package
<h1>Running the experiments</h1> <p>Prerequisites for running the experiments:</p> <ul> <li><a href="https://www.docker.com/" target="_blank" rel="noopener">Docker</a></li> <li><a href="https://python-poetry.org/" target="_blank" rel="noopener">Poetry</a></li> </ul> <p>Steps to run the experiments:</p> <ol> <li>Download the experiment zip file you wish to run (<code>single_parameter_experiment.zip</code> or <code>multi_parameter_experiment.zip</code>).</li> <li>Un-zip the file.</li> <li>Open a terminal and navigate to the unzipped folder (e.g. <code>cd single_parameter_experiment</code>).</li> <li>Run <code>poetry install --only main</code> to install all dependencies.</li> <li>To run the experiment, run <code>poetry run python run_experiment.py</code>.</li> <li>All results can be found in the folder <code>data/</code>.</li> </ol> <p>The modules used for the experiment are defined in the file <code>experiment_modules.py</code> and to see the experiment configuration, look in <code>run_experiment.py</code>.</p> <p><strong>Warning</strong>: the experiments take several weeks to run on a single machine, therefore it is advisable to split the experiments based on modules and run them in parallel.</p> <h1>Running the analysis</h1> <p>Prerequisites for running the analysis:</p> <ul> <li><a href="https://conda.io/projects/conda/en/latest/user-guide/getting-started.html" target="_blank" rel="noopener">Conda</a></li> </ul> <p>Steps to run the analysis:</p> <ol> <li>Download the analysis zip file (<code>analysis-adaptive-parameter-control.zip</code>).</li> <li>Un-zip the file.</li> <li>Open a terminal and navigate to the unzipped folder (e.g. <code>cd analysis-adaptive-parameter-control</code>).</li> <li>Run the following command to install the conda environment and all dependencies: <code>conda env create -f environment.yml</code></li> <li>If you want to re-run the Bayesian models locally on your machine, follow the <strong>optional</strong> step below, otherwise download and unzip the trace data from the replication package, i.e., <code>Trace data single.zip</code> and <code>Trace data multi.zip</code>. </li> <li>Place the <code>.nc</code> files in the corresponding folder: <code>analysis-adaptive-parameter-control/single_parameter/</code> or <code>analysis-adaptive-parameter-control/multi_parameter/</code>. <ol> <li>E.g. the <code>coverage_rate_model_single_parameter.nc</code> goes in the <code>single_parameter</code> folder, while the <code>coverage_rate_model_multi_parameter.nc</code> goes in the <code>multi_parameter</code> folder.</li> </ol> </li> <li>Navigate to the notebooks folder (<code>Notebooks/</code>).</li> <li>Open a notebook of choice (<code>coverage_rate_multi_parameter.ipynb</code>, <code>coverage_rate_single_parameter.ipynb</code>, <code>final_coverage_multi_parameter.ipynb</code>, <code>final_coverage_single_parameter.ipynb</code>, <code>overhead_model_multi_parameter.ipynb</code>, or <code>overhead_model_single_parameter.ipynb</code>).</li> <li>Navigate to the section called "Data analysis" and run all cells in order.</li> </ol> <h3>(Optional) Running the Bayesian models locally before the analysis.</h3> <ol> <li>Navigate to the notebooks folder (<code>Notebooks/</code>).</li> <li>Open a notebook of choice (<code>coverage_rate_multi_parameter.ipynb</code>, <code>coverage_rate_single_parameter.ipynb</code>, <code>final_coverage_multi_parameter.ipynb</code>, <code>final_coverage_single_parameter.ipynb</code>, <code>overhead_model_multi_parameter.ipynb</code>, or <code>overhead_model_single_parameter.ipynb</code>).</li> <li>Navigate to the section called "Model specification" and run the three notebook cells.</li> </ol> <p><strong>Warning</strong>: this will take a long time, if you don't have the time, use the following alternative instead</p> <h1>Data</h1> <p>The data from when we ran the experiments is available in the <code>Single data.zip</code> and <code>Multi data.zip</code> files.</p> <p>The structure of these are the following:</p> <ul> <li>There are folders for each module the experiment was run on, further divided into each unique run. All these folders include: <ul> <li>Coverage reports.</li> <li>Complete logs for the unique run.</li> <li>A timeline over controlled parameter values during the test generation process.</li> <li>The complete Pynguin configuration for the run.</li> <li>The generated test suite.</li> </ul> </li> <li>There is one <code>statistics.csv</code> file containing some information about each run and their branch coverage timelines.</li> </ul> <h1>Running the parameter assignment analysis</h1> <p>Prerequisites for running the parameter assignment analysis:</p> <ul> <li><a href="https://conda.io/projects/conda/en/latest/user-guide/getting-started.html" target="_blank" rel="noopener">Conda</a></li> </ul> <p>Steps to run the parameter assignment analysis:</p> <ol> <li>Download the analysis zip file (<code>parameter-assignment.zip</code>).</li> <li>Un-zip the file.</li> <li>Open a terminal and navigate to the unzipped folder (e.g. <code>cd parameter-assignment</code>).</li> <li>Run the following command to install the conda environment and all dependencies: <code>conda env create -f environment.yml</code></li> <li>Navigate to the notebooks folder (<code>Notebooks/</code>).</li> <li>Open the notebook <code>parameter_assignment_analysis.ipynb</code>.</li> <li>Run all cells in order.</li> </ol>
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.