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260 results for “Empirical Study”
Dataset of empirical study on product configuration and traceability in UML-based product-lines
<p>Dataset of empirical study on product configuration and traceability in UML-based product-lines</p>
Empirical Study of Restarted and Flaky Builds on Travis CI
<p>This upload contains the dataset that has been collected for the paper: Empirical Study of Restarted and Flaky Builds on Travis CI.</p> <p>It is composed of four elements:</p> <ul> <li>A dump of the MongoDB database that contains 3286773 TravisCI build and 56522 restarted builds (json.gz files)</li> <li>The Github information for the repositories (repositories.tar.gz)</li> <li>The complete TravisCI build history of the repositories (repository_build_histories.tar.gz)</li> <li>The pull request information including the comments (pull_requests.tar.gz)</li> <li>The complete commit history of the repositories (repository_commits.tar.gz)</li> </ul> <p> </p>
Empirical Study of the Relationship between Design Patterns and Code Smells
<p>Software systems are often developed in such a way that good practices in the object-oriented paradigm are not met, causing the occurrence of specific disharmonies which are sometimes called code smells. Design patterns catalogue best practices for developing object-oriented software systems. Although code smells and design patterns are widely divergent, there might be a co-occurrence relation between them. The objective of this paper is to empirically evaluate if the presence of design patterns is related to the presence of code smells at different granularity levels. We performed an empirical replication study using 20 design patterns and 13 code smells in ten small-size to medium-size, open-source Java-based systems. We applied statistical analysis and association rules. Results confirm that classes participating in design patterns have less smell-proneness and smell frequency than classes not participating in design patterns. We also noticed that every design pattern category act in the same way in terms of smell-proneness in the subject systems. However, we observed, based on the association rules learning and the proposed validation technique, that some patterns may be linked with certain smells in some cases. For instance, Command patterns can co-occur with God Class, Blob and External Duplication smell.</p> <p><strong>The published data set contains the following:</strong></p> <ol> <li>List of the selected systems (source code files)</li> <li>The P-MARt: the design pattern repository as XML for the selected systems. </li> <li>Data of design patterns and code smells: We processed this data by parsing the design pattern XML file and running the smell detection tool (inFusion).</li> <li>The data of the data mining analysis.</li> </ol>
What constitutes software? An Empirical, Descriptive Study of Artifacts - Reproduction Dataset
<p>Dataset for reproducing the numbers, figures, and tables in the results section of the MSR 2020 paper <em>"What constitutes Software? An Empirical, Descriptive Study of Artifacts"</em>.</p> <p>The data is given in this release as extra file (<code>all_repo_files_categorized.csv.bz2</code>) as it is too big to be part of the repository directly.</p>
Assessing confidence in root placement on phylogenies: an empirical study using non-reversible models for Mammals
<p>Using time-reversible Markov models is a very common practice in phylogenetic analysis, because although we expect many of their assumptions to be violated by empirical data, they provide high computational efficiency. However, these models lack the ability to infer the root placement of the estimated phylogeny. In order to compensate for the inability of these models to root the tree, many researchers use external information such as using outgroup taxa or additional assumptions such as molecular-clocks. In this study, we investigate the utility of non-reversible models to root empirical phylogenies and introduce a new bootstrap measure, the <i>rootstrap</i>, which provides information on the statistical support for any given root position.</p> <p>Availability and implementation: rootstrap support is implemented in IQ-TREE 2 and a tutorial is available at the iqtree webpage <a href="http://www.iqtree.org/doc/Rootstrap">http://www.iqtree.org/doc/Rootstrap</a>. In addition, a python script is available at <a href="https://github.com/suhanaser/Rootstrap">https://github.com/suhanaser/Rootstrap</a></p>
Data in the analysis of Comparative Study of the Impact of Dummy Variables on Regression Coefficients and Canonical Correlation Indices: an Empirical perspective
<p>This data is used in the analysis related to the article Comparative Study of the Impact of Dummy Variables on Regression Coefficients and Canonical Correlation Indices: an Empirical perspective. The data contain Crude Oil Prices, Exchange Rates, and Dummy variables coded 1 for the era before the Covid-19 outbreak and 0 for the era of Covid-19.</p> <p> </p>
Screen Capture & Audior Recordings - Empirical Study "Business Process Model Validation Through Virtual Enactment"
<p>Screen captures of task completion and audio recordings of interviews of the empirical study which has been conducted within the context of the master thesis "Business Process Model Validation Through Virtual Enactment"</p>
Replication package for the paper :The Relationship Between Different Python Argument-Passing Mechanisms and Fixes: An Empirical Study
<p><strong>Abstract:</strong></p> <p>Modern programming languages, such as Python, have introduced a variety of constructs and syntactical elements to make software development more efficient and concise. Examples include lambda functions, comprehension collections, or mechanisms to facilitate the passing of arguments to a function. While many of such constructs may, in principle, be beneficial for developers, recent studies have shown that certain programming constructs may affect program understanding and even induce more fixes than other changes. <br>This paper studies the effect of different Python argument-passing mechanisms to investigate their relationship with code proneness to be fixed. Specifically, we study the fix-proneness for what concerns function definitions and invocations. This is done by analyzing the evolutionary history of 200 Python projects, for a total of about 3M functions and 12M call sites. While there are varying effects for what concerns parameter declaration mechanisms, we found evidence that keyword-based argument passing is less defect-prone than positional argument passing, and this is not affected by size-related confounding factors.</p>
Dataset of the paper: "How do Hugging Face Models Document Datasets, Bias, and Licenses? An Empirical Study"
<p>This replication package contains datasets and scripts related to the paper: "*How do Hugging Face Models Document Datasets, Bias, and Licenses? An Empirical Study*"</p><p> </p><p>## Root directory</p><p>- `statistics.r`: R script used to compute the correlation between usage and downloads, and the RQ1/RQ2 inter-rater agreements</p><p>- `modelsInfo.zip`: zip file containing all the downloaded model cards (in JSON format)</p><p>- `script`: directory containing all the scripts used to collect and process data. For further details, see README file inside the script directory.</p><p> </p><p>## Dataset</p><p>- `Dataset/Dataset_HF-models-list.csv`: list of HF models analyzed</p><p>- `Dataset/Dataset_github-prj-list.txt`: list of GitHub projects using the *transformers* library</p><p>- `Dataset/Dataset_github-Prj_model-Used.csv`: contains usage pairs: project, model</p><p>- `Dataset/Dataset_prj-num-models-reused.csv`: number of models used by each GitHub project</p><p>- `Dataset/Dataset_model-download_num-prj_correlation.csv` contains, for each model used by GitHub projects: the name, the task, the number of reusing projects, and the number of downloads</p><p> </p><p> </p><p>## RQ1</p><p>- `RQ1/RQ1_dataset-list.txt`: list of HF datasets</p><p>- `RQ1/RQ1_datasetSample.csv`: sample set of models used for the manual analysis of datasets</p><p>- `RQ1/RQ1_analyzeDatasetTags.py`: Python script to analyze model tags for the presence of datasets. it requires to unzip the `modelsInfo.zip` in a directory with the same name (`modelsInfo`) at the root of the replication package folder. Produces the output to stdout. To redirect in a file fo be analyzed by the `RQ2/countDataset.py` script</p><p>- `RQ1/RQ1_countDataset.py`: given the output of `RQ2/analyzeDatasetTags.py` (passed as argument) produces, for each model, a list of Booleans indicating whether (i) the model only declares HF datasets, (ii) the model only declares external datasets, (iii) the model declares both, and (iv) the model is part of the sample for the manual analysis</p><p>- `RQ1/RQ1_datasetTags.csv`: output of `RQ2/analyzeDatasetTags.py`</p><p>- `RQ1/RQ1_dataset_usage_count.csv`: output of `RQ2/countDataset.py`</p><p> </p><p> </p><p>## RQ2</p><p>- `RQ2/tableBias.pdf`: table detailing the number of occurrences of different types of bias by model Task</p><p>- `RQ2/RQ2_bias_classification_sheet.csv`: results of the manual labeling</p><p>- `RQ2/RQ2_isBiased.csv`: file to compute the inter-rater agreement of whether or not a model documents Bias</p><p>- `RQ2/RQ2_biasAgrLabels.csv`: file to compute the inter-rater agreement related to bias categories</p><p>- `RQ2/RQ2_final_bias_categories_with_levels.csv`: for each model in the sample, this file lists (i) the bias leaf category, (ii) the first-level category, and (iii) the intermediate category</p><p> </p><p> </p><p>## RQ3</p><p>- `RQ3/RQ3_LicenseValidation.csv`: manual validation of a sample of licenses</p><p>- `RQ3/RQ3_{NETWORK-RESTRICTIVE|RESTRICTIVE|WEAK-RESTRICTIVE|PERMISSIVE}-license-list.txt`: lists of licenses with different permissiveness</p><p>- `RQ3/RQ3_prjs_license.csv`: for each project linked to models, among other fields it indicates the license tag and name</p><p>- `RQ3/RQ3_models_license.csv`: for each model, indicates among other pieces of info, whether the model has a license, and if yes what kind of license</p><p>- `RQ3/RQ3_model-prj-license_contingency_table.csv`: usage contingency table between projects' licenses (columns) and models' licenses (rows)</p><p>- `RQ3/RQ3_models_prjs_licenses_with_type.csv`: pairs project-model, with their respective licenses and permissiveness level</p><p> </p><p>## scripts</p><p>Contains the scripts used to mine Hugging Face and GitHub. Details are in the enclosed README</p><p> </p>
Dataset for the Paper: Issues and Their Causes in WebAssembly Applications: An Empirical Study
<p>This dataset accompanies the paper titled 'Issues and Their Causes in WebAssembly Applications: An Empirical Study.' The dataset is stored in a Microsoft Excel file, which comprises multiple worksheets. A brief description of each worksheet is provided below.</p><p>(1) The '<strong>Selected Systems</strong>' worksheet contains information on the 12 chosen open-source WebAssembly applications, along with the URL for each application.</p><p>(2) The '<strong>GitHub-Raw Data</strong>' worksheet contains information on the initially retrieved 6,667 issues, including the titles, links, and statuses of each individual issue discussion.</p><p>(3) The '<strong>SOF-Raw Data</strong>' worksheet contains information on the initially retrieved 6,667 questions and answers, including the details of each question and answer, respective links, and associated tags.</p><p>(4) The '<strong>GitHubData Random Selected</strong>' worksheet contains a list of issues randomly selected from the initial pool of 6,667 issues, as well as extracted data from the discussions associated with these randomly selected issues.</p><p>(5) The '<strong>GitHub-(Issues, Causes)</strong>' worksheet contains the initial codes categorizing the types of issues and causes.</p><p>(6) The '<strong>SOF (Issues, Causes)</strong>' worksheet contains information gleaned from a randomly selected subset of 354 Stack Overflow posts. This information includes the title and body of each question, the associated link, tags, as well as key points for types of issues and causes.</p><p>(7) The '<strong>Combine (Git and SOF) Data</strong>' worksheet contains the compiled issues and causes extracted from both GitHub and Stack Overflow.</p><p>(8) The <strong>'Issue Taxonomy</strong>' worksheet contains a comprehensive issue taxonomy, which is organized into 9 categories, 20 subcategories, and 132 specific types of issues.</p><p>(9) The '<strong>Cause Taxonomy</strong>' worksheet contains a comprehensive cause taxonomy, which is organized into 10 categories, 35 subcategories, and 283 specific types of causes.</p>
Replication Package for "Benefits and pitfalls of token-level SZZ: An empirical study on OSS projects"
<p>Replication Package for "Benefits and pitfalls of token-level SZZ: An empirical study on OSS projects"</p><p>All materials are licensed under the MIT License (see LICENSE file). </p>
Artifact for "Inside Bug Report Templates: An Empirical Study on Bug Report Templates in Open-Source Software"
<p>This is the artifact for the paper "Inside Bug Report Templates: An Empirical Study on Bug Report Templates in Open-Source Software".</p> <p><strong>What the artifact does:</strong><br>1) a questionnaire that we used for our online survey (PDF);<br>2) the valid responses of our online survey (CSV).</p> <p>3) the code of preprocessing (.py).</p> <p>4) the dataset of preprocessing and labeling (CSV).</p>
On the Need to Monitor Continuous Integration Practices: An Empirical Study
<p>This repository contains all the artifacts and data related to the <strong>On the Need to Monitor Continuous Integration Practices: An Empirical Study</strong>. The folder structure and their entailments are as follows:</p> <ul> <li><strong>data</strong>: Contains .sql files with the dataset collected during the study, which includes all project information retrieved from GitHub. A README.txt file inside the folder explains how to restore the dataset.</li> <li><strong>document_analysis</strong>: Includes the analysis files for specific research questions: <ul> <li>RQ1 subdirectory: Contains the pull request (PR) comments dataset and an Excel file with the analysis data for Research Question 1.</li> <li>RQ3 subdirectory: Contains documentation from CI services and third-party tools analyzed in Research Question 3, along with an Excel file summarizing the analysis data.</li> </ul> </li> <li><strong>scripts</strong>: Provides the R scripts used for data analysis and chart generation.</li> <li><strong>survey</strong>: Includes the collected survey responses and a template of the survey sent to developers.</li> </ul> <p><br>The total size of the repository when decompressed is approximately <strong>8.14 GB</strong>.</p>
COVID-19 evidence syntheses with artificial intelligence: an empirical study of systematic reviews
<p><strong>Objectives</strong>: A rapidly developing scenario like a pandemic requires the prompt production of high-quality systematic reviews, which can be automated using artificial intelligence (AI) techniques. We evaluated the application of AI tools in COVID-19 evidence syntheses.</p> <p><strong>Study design</strong>: After prospective registration of the review protocol, we automated the download of all open-access COVID-19 systematic reviews in the COVID-19 Living Overview of Evidence database, indexed them for AI-related keywords, and located those that used AI tools. We compared their journals' JCR Impact Factor, citations per month, screening workloads, completion times (from pre-registration to preprint or submission to a journal) and AMSTAR-2 methodology assessments (maximum score 13 points) with a set of publication date matched control reviews without AI.</p> <p><strong>Results</strong>: Of the 3999 COVID-19 reviews, 28 (0.7%, 95% CI 0.47-1.03%) made use of AI. On average, compared to controls (n=64), AI reviews were published in journals with higher Impact Factors (median 8.9 vs 3.5, P<0.001), and screened more abstracts per author (302.2 vs 140.3, P=0.009) and per included study (189.0 vs 365.8, P<0.001) while inspecting less full texts per author (5.3 vs 14.0, P=0.005). No differences were found in citation counts (0.5 vs 0.6, P=0.600), inspected full texts per included study (3.8 vs 3.4, P=0.481), completion times (74.0 vs 123.0, P=0.205) or AMSTAR-2 (7.5 vs 6.3, P=0.119).</p> <p><strong>Conclusion</strong>: AI was an underutilized tool in COVID-19 systematic reviews. Its usage, compared to reviews without AI, was associated with more efficient screening of literature and higher publication impact. There is scope for the application of AI in automating systematic reviews.</p>
An Empirical Study on the Usage and Availability of Machine Learning Libraries in Open-Source Python Projects - Dataset
<p>This repository contains the dataset of the manuscript:</p> <p>"An Empirical Study on the Usage and Availability of Machine Learning Libraries in Open-Source Python Projects"</p>
Dataset of the paper "An Empirical Study on the Fault-Inducing Effect of Functional Constructs in Python"
<p>This package contains the dataset of the manuscript "An Empirical Study on the Fault-Inducing Effect of Functional Constructs in Python"</p>
MapOSR - A Mapping Review Dataset of Empirical Studies on Open Science
<p>Research that investigates respective researchers’ engagement in Open Science varies widely in the topics addressed, methods employed, and disciplines investigated, which makes it difficult to integrate and compare its results. To investigate current outcomes of Open Science research, and to get a better understanding on topicswell-researched and on research gaps we aim at providing an openly accessible overview of empirical studies that focus on different aspects of Open Science in different scientific disciplines, academic groups and geographical regions. The present data set of studies about Open Science practices was retrieved following a PRISM approach to compile a literature review. We include studies from the Scopus and Web of Science databases with keywords relating to Open Science between the years 2000 and 2020, as well as a snowball search for relevant articles. Studies that did not investigate any aspect of Open Science, or weren’t peer reviewed were excluded, resulting in a total of 695 remaining studies. The data set was collaboratively annotated to ensure intercoder reliability of the coded data.</p>
ASE_2022_A Tale of Two Cities: an Empirical Study on Deep Learning OSS Communities
<p>The dataset of paper---ASE_2022_A Tale of Two Cities: an Empirical Study on Deep Learning OSS Communities.</p> <p>It includes 14,053 and 21,765 contributors, as well as 23,739 and 33,454 issues from the PyTorch and TensorFlow communities. </p>
ASE_2022_A Tale of Two Cities: an Empirical Study on Deep Learning OSS Communities
<p>The dataset of paper---ASE_2022_A Tale of Two Cities: an Empirical Study on Deep Learning OSS Communities.</p> <p>It includes 14,053 and 21,765 contributors, as well as 23,739 and 33,454 issues from the PyTorch and TensorFlow communities. </p>
ASE_2022_A Tale of Two Cities: an Empirical Study on Deep Learning OSS Communities
<p>The dataset of paper---ASE_2022_A Tale of Two Cities: an Empirical Study on Deep Learning OSS Communities.</p> <p>It includes 23,739 and 33,454 issues from the PyTorch and TensorFlow communities. </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.