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61 results for “Open Source Software”
The potential of low-cost UAVs and open-source photogrammetry software for high-resolution monitoring of alpine glaciers: A case study from the Kanderfirn (Swiss Alps)
<p>This dataset contains high-resolution orthophotos (5 x 5 cm) and digital surface models (25 x 25 cm) of the Kandernfirn Glacier located in the Swiss Alps. Aerial images were aquired with a self-developed fixed-wing Unmanned Aerial Vehicle during ten surveys on five different days in 2017 and 2018. The open-source photogrammetry software OpenDroneMap (version 0.4.1) was used for image processing.</p> <p>The orthophotos and digital surface models were validated through dGNSS point measurements of ground control points. Please refer to the corresponding paper for information on the horizontal and vertical accuracy of the files.</p>
Dataset for: Evaluating phylogenetic methods for quantifying risks and opportunities presented by forks in open source software (master dissertation).
<p>This is the data for my master dissertation [1]. If you wish to get a copy, download it from Zenodo and open docs/master.pdf.</p> <p>Data acquisition and encoding techniques are described in paragraph 3.1.1 (table 3.1).</p> <p>The data is described in more detail in paragraph 4.1 (table 4.2).</p> <p>* fork1_all.csv: MySQL server / MariaDB server<br> * fork2_all.csv: Linux kernel / Android kernel<br> * fork3_all.csv: Apache OpenOffice / LibreOffice</p> <p>==Cite==<br> [1] A. Ortiz-Troncoso. Evaluating phylogenetic methods for quantifying risks and opportunities presented<br> by forks in open source software (master dissertation). Zenodo, 2018. doi: http://doi.org/10.5281/zenodo.1158292</p>
Enterprise-Driven Open Source Software
<p>We present a dataset of open source software developed mainly by enterprises rather than volunteers. This can be used to address known generalizability concerns, and, also, to perform research on open source business software development. Based on the premise that an enterprise's employees are likely to contribute to a project developed by their organization using the email account provided by it, we mine domain names associated with enterprises from open data sources as well as through white- and blacklisting, and use them through three heuristics to identify 17,264 enterprise GitHub projects. We provide these as a dataset detailing their provenance and properties. A manual evaluation of a dataset sample shows an identification accuracy of 89%. Through an exploratory data analysis we found that projects are staffed by a plurality of enterprise insiders, who appear to be pulling more than their weight, and that in a small percentage of relatively large projects development happens exclusively through enterprise insiders.</p> <p>The main dataset is provided as a 17,264 record tab-separated file named <code>enterprise_projects.txt</code> with the following 29 fields.</p> <ul> <li><strong>url</strong>: the project's GitHub URL</li> <li><strong>project_id</strong>: the project's GHTorrent identifier</li> <li><strong>sdtc</strong>: true if selected using the same domain top committers heuristic (9,016 records)</li> <li><strong>mcpc</strong>: true if selected using the multiple committers from a valid enterprise heuristic (8,314 records)</li> <li><strong>mcve</strong>: true if selected using the multiple committers from a probable company heuristic (8,015 records),</li> <li><strong>star_number</strong>: number of GitHub watchers</li> <li><strong>commit_count</strong>: number of commits</li> <li><strong>files</strong>: number of files in current main branch</li> <li><strong>lines</strong>: corresponding number of lines in text files</li> <li><strong>pull_requests</strong>: number of pull requests</li> <li><strong>github_repo_creation</strong>: timestamp of the GitHub repository creation</li> <li><strong>earliest_commit</strong>: timestamp of the earliest commit</li> <li><strong>most_recent_commit</strong>: date of the most recent commit</li> <li><strong>committer_count</strong>: number of different committers</li> <li><strong>author_count</strong>: number of different authors</li> <li><strong>dominant_domain</strong>: the projects dominant email domain</li> <li><strong>dominant_domain_committer_commits</strong>: number of commits made by committers whose email matches the project's dominant domain</li> <li><strong>dominant_domain_author_commits</strong>: corresponding number for commit authors</li> <li><strong>dominant_domain_committers</strong>: number of committers whose email matches the project's dominant domain</li> <li><strong>dominant_domain_authors</strong>: corresponding number for commit authors</li> <li><strong>cik</strong>: SEC's EDGAR "central index key"</li> <li><strong>fg500</strong>: true if this is a Fortune Global 500 company (2,233 records)</li> <li><strong>sec10k</strong>: true if the company files SEC 10-K forms (4,180 records)</li> <li><strong>sec20f</strong>: true if the company files SEC 20-F forms (429 records)</li> <li><strong>project_name</strong>: GitHub project name</li> <li><strong>owner_login</strong>: GitHub project's owner login</li> <li><strong>company_name</strong>: company name as derived from the SEC and Fortune 500 data</li> <li><strong>owner_company</strong>: GitHub project's owner company name</li> <li><strong>license</strong>: SPDX license identifier</li> </ul> <p>The file <code>cohost_project_details.txt</code> provides the full set of 311,223 cohort projects that are not part of the enterprise data set, but have comparable quality attributes.</p> <ul> <li><strong>url</strong>: the project's GitHub URL</li> <li><strong>project_id</strong>: the project's GHTorrent identifier</li> <li><strong>stars</strong>: number of GitHub watchers</li> <li><strong>commit_count</strong>: number of commits</li> </ul>
The Debsources Dataset: Two Decades of Free and Open Source Software
<p>This is the Debsources Dataset: source code and related metadata spanning two decades of Free and Open Source Software (FOSS) history, seen through the lens of the Debian distribution.</p> <p>The dataset spans more than 3 billion lines of source code as well as metadata about them such as: size metrics (lines of code, disk usage), developer-defined symbols (ctags), file-level checksums (SHA1, SHA256, TLSH), file media types (MIME), release information (which version of which package containing which source code files has been released when), and license informa-<br> tion (GPL, BSD, etc).</p> <p>The Debsources Dataset comes as a set of tarballs containing deduplicated unique source code files organized by their SHA1 checksums (the source code), plus a portable PostgreSQL database dump (the metadata).</p> <p>The Debsources Dataset is described in full in the paper The Debsources Dataset: Two Decades of Free and Open Source Software, published on the Empirical Software Engineering journal with DOI 10.1007/s10664-016-9461-5 . A preprint of the paper is available at https://upsilon.cc/~zack/research/publications/debsources-ese-2016.pdf .</p>
Dataset of Open-Source Software Developers Labeled by their Experience Level and Associated with their Software Metrics
<p>This dataset contains 703 anonymized developers extracted from 17 open-source projects from GitHub. Projects were chosen because they use:</p> <ul> <li>the Java programming language</li> <li>the <a href="https://spring.io/projects/spring-framework">Spring framework</a></li> <li><a href="https://maven.apache.org/">Maven</a> / <a href="https://gradle.org/">Gradle</a> build tools</li> </ul> <p>For all these developers, 23 software metrics were calculated for each project to which they contribute. These metrics are either calculated by analyzing the source code or relative to project management metadata. Each of these developers then have been manually annotated. To do this, developers have been searched for in professionnal social media such as:</p> <ul> <li><a href="https://www.linkedin.com/">Linkedin</a></li> <li><a href="https://twitter.com/">Twitter</a></li> <li><a href="https://github.com/">Github</a></li> </ul> <p><strong>This dataset is published in the following journal article: </strong></p> <p><strong>Dataset of Open-Source Software Developers Labeled by their Experience Level in the Project and their Associated Software Metrics, Q. Perez, C. Urtado and </strong><strong>S. Vauttier, Data In Brief, </strong></p> <p><a href="https://www.sciencedirect.com/science/article/pii/S2352340922010459">https://www.sciencedirect.com/science/article/pii/S2352340922010459</a></p>
Citations to software and data in Zenodo via open sources
<p>In January 2019, the Asclepias Broker harvested citation links to Zenodo objects from three discovery systems: the NASA Astrophysics Datasystem (ADS), Crossref Event Data and Europe PMC. Each row of our dataset represents one unique link between a citing publication and a Zenodo DOI. Both endpoints are described by basic metadata. The second dataset contains usage metrics for every cited Zenodo DOI of our data sample. </p> <p> </p>
Dataset of the paper "An Empirical Characterization of Software Bugs in Open-Source Cyber-Physical Systems"
<p><br> #Dataset Package for the paper "An Empirical Characterization of Software Bugs in Open-Source Cyber-Physical Systems"</p> <p><br> Description of the content:</p> <p><br> 1) "1_RQ-CPS-bugs-Taxonomy" folder contains all the main experimental data concerning the issues sampled and analyzed from all the Projects considered in the study,<br> including row-data on the taxonomy validtion steps.<br> <br> - Under "the sub-folder "1_Taxonomy-Raw-data" are reported the row-data concerning the taxonomy validtion steps </p> <p><br> 2) "2_Scripts" contains all scripts used to generate the issue data and sampled issue raw-data in the previous folders: </p> <p><br> - "setup.md" file in the folder describes how to set=up and run the script used for collecting and sampling the issues for the validation steps:<br> <br> - runJSONtoCSV.sh<br> - JSONtoCSV.py<br> - generateListOfAllSamples.py<br> - generateAllSamples.r<br> <br> Under "the sub-folder "1_Scripts/1_Data_Collection":<br> <br> <br> 3) "3_Final Taxonomy" folder contains the final Table representation (also reported in the previous folder) and main figures of the CPSs Bugs Taxonomy.</p>
Bee Tracker – an open-source machine-learning based video analysis software for the assessment of nesting and foraging performance of cavity-nesting solitary bees
<p>The foraging and nesting performance of bees can provide important information on bee health and is of interest for risk and impact assessment of environmental stressors. While radio-frequency identification (RFID) technology is an efficient tool increasingly used for the collection of behavioral data in social bee species such as honey bees, behavioral studies on solitary bees still largely depend on direct observations, which is very time-consuming.</p> <p>Here, we present a novel automated methodological approach of individually and simultaneously tracking and analyzing foraging and nesting behavior of numerous cavity-nesting solitary bees. The approach consists of monitoring nesting units by video recording and automated analysis of videos by a machine learning based software. This <i>Bee Tracker</i> software consists of four trained deep learning networks to detect bees that enter or leave their nest and to recognize individual IDs on the bees' thorax as well as the IDs of their nests according to their positions in the nesting unit.</p> <p>The software is able to identify each nest of each individual nesting bee, which permits to measure individual-based measures of reproductive success. Moreover, the software quantifies the number of cavities a female enters until it finds its nest as a proxy of nest recognition, and it provides information on the number and duration of foraging trips. By training the software on 8 videos recording 24 nesting females per video, the software achieved a precision of 96% correct measurements of these parameters.</p> <p>The software could be adapted to various experimental setups by training it to an according set of videos. The presented method allows to efficiently collect large amounts of data on cavity-nesting solitary bee species and represents a promising new tool for the monitoring and assessment of behavior and reproductive success under laboratory, semi-field and field conditions.</p>
Free Open Source Communities Sustainability: Does It Make a Difference in Software Quality?
<p><strong>Context:</strong> Free and Open Source Software (FOSS) communities' ability to stay viable and productive over time is pivotal for society as they maintain the building blocks that digital infrastructure, products, and services depend on. Sustainability may, however, be characterized from multiple aspects, and less is known how these aspects interplay and impact community outputs, and software quality specifically.</p> <p><strong>Objective: </strong>This study, therefore, aims to empirically explore how the different aspects of FOSS sustainability impact software quality.</p> <p><strong>Method: </strong>16 sustainability metrics across four categories were sampled and applied to a set of 217 OSS projects sourced from the Apache Software Foundation Incubator program. The impact of a decline in the sustainability metrics was analyzed against eight software quality metrics using Bayesian data analysis, which incorporates probability distributions to represent the regression coefficients and intercepts.</p> <p><strong>Results: </strong>Findings suggest that selected sustainability metrics do not significantly affect defect density or code coverage. However, a positive impact of community age was observed on specific code quality metrics, such as risk complexity, number of very large files, and code duplication percentage. Interestingly, findings show that even when communities are experiencing sustainability, certain code quality metrics are negatively impacted.</p> <p><strong>Conclusion:</strong> Findings imply that code quality practices are not consistently linked to sustainability, and defect management and prevention may be prioritized over the former. Results suggest that growth, resulting in a more complex and large codebase, combined with a probable lack of understanding of code quality standards, may explain the degradation in certain aspects of code quality.</p>
CVEfixes Dataset: Automatically Collected Vulnerabilities and Their Fixes from Open-Source Software
<p><em>CVEfixes</em> is a comprehensive vulnerability dataset that is automatically collected and curated from Common Vulnerabilities and Exposures (CVE) records in the public <a href="https://nvd.nist.gov/">U.S. National Vulnerability Database (NVD)</a>. The goal is to support data-driven security research based on source code and source code metrics related to fixes for CVEs in the NVD by providing detailed information at different interlinked levels of abstraction, such as the commit-, file-, and method level, as well as the repository- and CVE level.</p> <p>This release, v1.0.8, covers all published CVEs up to 23 July 2024. All open-source projects that were reported in CVE records in the NVD in this time frame _and_ had publicly available git repositories were fetched and considered for the construction of this vulnerability dataset. The dataset is organized as a relational database and covers 12107 vulnerability fixing commits in 4249 open source projects for a total of 11873 CVEs in 272 different Common Weakness Enumeration (CWE) types. The dataset includes the source code before and after changing 51342 files and 138974 functions. The collection took 48 hours with 4 workers (AMD EPYC Genoa-X 9684X).</p> <p>This repository includes the SQL dump of the dataset, as well as the JSON for the CVEs and XML of the CWEs at the time of collection. The complete process has been documented in the paper <em>"CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software"</em>, which is published in the Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE '21). You will find a copy of the paper in the Doc folder. </p> <p><em><strong>Citation and Zenodo links</strong></em></p> <p>Please cite this work by referring to the published paper:</p> <ul> <li>Guru Bhandari, Amara Naseer, and Leon Moonen. 2021. CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software. In Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE '21). ACM, 10 pages. <a href="https://doi.org/10.1145/3475960.3475985">https://doi.org/10.1145/3475960.3475985</a></li> </ul> <pre><code>@inproceedings{bhandari2021:cvefixes, title = {{CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software}}, booktitle = {{Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE '21)}}, author = {Bhandari, Guru and Naseer, Amara and Moonen, Leon}, year = {2021}, pages = {10}, publisher = {{ACM}}, doi = {10.1145/3475960.3475985}, copyright = {Open Access}, isbn = {978-1-4503-8680-7}, language = {en} }</code></pre> <p>The dataset has been released on Zenodo with DOI:<a href="https://doi.org/10.5281/zenodo.4476563">10.5281/zenodo.4476563</a>. The GitHub repository containing the code to automatically collect the dataset can be found at <a href="https://github.com/secureIT-project/CVEfixes">https://github.com/secureIT-project/CVEfixes</a>, released with DOI:<a href="https://doi.org/10.5281/zenodo.5111494">10.5281/zenodo.5111494</a>.</p>
On the Popularity of Modern Open Source Software
<p><strong>This dataset contains the data analyzed on the paper:</strong></p> <p>Hudson Borges and Marco Tulio Valente. <em>On the Popularity of Modern Open Source Software</em>. Submitted to Journal of Systems and Software (JSS), 2018.</p> <p><strong>Files:</strong></p> <ul> <li><em>cdf.csv</em>: Cumulative distribution function data.</li> <li><em>contributos.csv</em>: List of contributors of the repositories.</li> <li><em>developers_perceptions.csv</em>: Survey of Developers' Perceptions on Growth Patterns.</li> <li><em>factors.[activity,owner,repository].csv</em>: Additional information of the analyzed repositories and their owners.</li> <li><em>growth_patterns.zip</em>: A compressed file containing the output of the KSC algorithm (time series clusters).</li> <li><em>motivations_for_starring.csv</em>: Developers' motivations for starring projects.</li> <li><em>owners.csv</em>: Information of the repositories' owners.</li> <li><em>releases.csv</em>: Releases considered in the study (i.e., major and minor releases only).</li> <li><em>repositories.csv</em>: Information of the analyzed repositories (e.g., stars, forks, owner, creation date, etc.).</li> <li><em>timeseries.json</em>: File containing the number of stars gained by week for each repository since their creation.</li> </ul>
Quality and Success in Open Source Software: A Systematic Mapping
<p>Reference_List.pdf includes all of the articles that is related with the study, "Quality and Success in Open Source Software: A Systematic Mapping".</p> <p>Reference_List2.pdf includes one part of Reference_List (37 articles) and all of the articles that included in the study, "A Review on the Relation of Quality and Success in Open Source Software".</p>
Replication Package for ICSE'21 paper - Representation of Developer Expertise in Open Source Software
<p>Replication package for ICSE'21 paper: Representation of Developer Expertise in Open Source Software.</p> <p>See README for details.</p>
Quality Assurance Awareness in Open Source Software Projects on GitHub Analysis Dataset
<p>Dataset for the paper "Quality Assurance Awareness in Open Source Software Projects on GitHub", submitted to the 23rd IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM), 2023.</p>
Bee Tracker – an open-source machine-learning based video analysis software for the assessment of nesting and foraging performance of cavity-nesting solitary bees
Open the record for dataset details and reuse information.
Quality Evaluation Models or Frameworks for Open Source Software: A Systematic Literature Review (Article Pool)
<p>This pdf includes all of the articles that analyzed in the study: "Quality Evaluation Models or Frameworks for Open Source Software: A Systematic Literature Review".</p>
Open-source software collaboration network mining dataset
<p>The resulting dataset of the <a href="https://github.com/gotec/git2net">git2net </a>and <a href="https://github.com/wschuell/repo_tools">repo_tools </a>mining process for randomly selected large open-source repositories.</p>
Mandelbugs in Open-Source Software
<p>This dataset contains a list bugs from four open-source projects (the Linux kernel, the MySQL DBMS, the Apache HTTPD web server, and the Apache AXIS WS framework). The bugs have been classified into Mandelbugs, Bohrbugs, or Aging-Related Bugs, by analyzing the conditions that exercise the bug (i.e., the "fault trigger"). This classification is useful to get insights into bugs and failures that can occur in OSS projects, and to tune testing and fault-tolerance strategies according to the distribution of bug types in a project.</p> <p>The dataset contains an ARFF file for each subsystem of the four open-source projects. Each row of the ARFF file contains:</p> <p>- An IDs of the bug, which can be used to retrieve more information about the bug from the issue tracker of the project;</p> <p>- A string that represents the class of the bug (BOH = Bohrbug; NAM = Mandelbug; ARB = Aging-Related Bug; UNK = Unknown class);</p> <p>- A string that represents the sub-class of the bug (for Bohrbugs, the sub-class is not available; the subclasses for Mandelbugs are LAG, ENV, TIM, SEQ; the subclasses for Aging-Related bugs are MEM, STO, LOG, NUM, TOT).<br> </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>
Quality Evaluation Models or Frameworks for Open Source Software: A Systematic Literature Review (Matching articles with categories)
<p>This document includes which primary study falls into which category with respect to the RQs in the following study: “Quality Evaluation Models or Frameworks for Open Source Software: A Systematic Literature Review”</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.
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International Brain Laboratory public data
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OpenNeuro
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