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23 results for “software repository”
GitHub Profiles (users/organisations) and Repositories (research/non-research) of Potsdam Researchers and Research Organisations: An annotated dataset of with howfairis and software quality variables.
<p>This dataset accompanies the paper <em>"Software FAIRness, Documentation and Development Practices in Potsdam Researchers' GitHub Repositories"</em> It includes 3 CSV files that contain data related to github profiles of users/organisations, their repositories annotated as research/non-research repositories and followed by FAIRness and other software qualtiy variables. The data were collected using <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP">SWORDS-template-UP</a> (v1.0.0) methods (collect_users, collect_repositories, collect_variables) which is extended version of <a href="https://github.com/UtrechtUniversity/SWORDS-template">SWORS-template</a> adopted according our needs and detailed in the paper.</p> <p><strong>GitHub (research) user/organisation profiles. ( <em>github_profiles.csv )</em></strong></p> <table> <tbody> <tr> <td><strong>Column name</strong></td> <td><strong>Description </strong></td> </tr> <tr> <td>user_id</td> <td>GitHub username </td> </tr> <tr> <td>html_url </td> <td>URL of the GitHub profile </td> </tr> <tr> <td>type </td> <td>Type of profile (user or organization)</td> </tr> <tr> <td>organisation</td> <td>Acronym or name of the organization </td> </tr> </tbody> </table> <p><strong>GitHub repositories <em>(github_repositories.csv)</em></strong></p> <p>This file contains the repositories scraped from the GitHub profiles of research users and organizations.</p> <table> <tbody> <tr> <td><strong>Column name </strong></td> <td><strong>Description </strong></td> </tr> <tr> <td>html_url </td> <td>URL link to the repository </td> </tr> <tr> <td>description</td> <td>GitHub project description </td> </tr> <tr> <td>project</td> <td>Specifies if the project is research or non-research</td> </tr> <tr> <td>language</td> <td>Programming language used in the project </td> </tr> <tr> <td>organisation</td> <td>Acronym or name of the university, institution, or research organization</td> </tr> <tr> <td>research_group</td> <td>Acronym or name of the research group the repository belongs to</td> </tr> </tbody> </table> <p><strong>Research repositories filtered and annotated <em>(github_research_repositories_filtered_annotated.csv)</em></strong></p> <p>This file contains filtered and annotated information about research repositories.</p> <table> <tbody> <tr> <td><strong>Column Name </strong></td> <td><strong>Description </strong></td> <td><strong>Collection Method </strong></td> </tr> <tr> <td>html_url </td> <td>Repository URL </td> <td> </td> </tr> <tr> <td>howfairis_repository</td> <td>Indicates if the repository is public or private (True/False) </td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/tree/main/collect_variables#usage">howfairis_variable.py</a>) is a wrapper for <a href="https://pypi.org/project/howfairis/">howfairis</a> pypi library that checks the 5 recommendations of <a href="https://fair-software.nl">FAIR</a></td> </tr> <tr> <td>howfairis_license </td> <td>Indicates if the repository has a license (True/False)</td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/tree/main/collect_variables#usage">howfairis_variable.py</a>) is a wrapper for <a href="https://pypi.org/project/howfairis/">howfairis</a> pypi library that checks the 5 recommendations of <a href="https://fair-software.nl">FAIR</a></td> </tr> <tr> <td>howfairis_registry</td> <td>Indicates if the repository has implemented community registry (True/False)</td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/tree/main/collect_variables#usage">howfairis_variable.py</a>) is a wrapper for <a href="https://pypi.org/project/howfairis/">howfairis</a> pypi library that checks the 5 recommendations of <a href="https://fair-software.nl">FAIR</a></td> </tr> <tr> <td>howfairis_citation</td> <td>Indicates if the repository has a .cff file (True/False) </td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/tree/main/collect_variables#usage">howfairis_variable.py</a>) is a wrapper for <a href="https://pypi.org/project/howfairis/">howfairis</a> pypi library that checks the 5 recommendations of <a href="https://fair-software.nl">FAIR</a></td> </tr> <tr> <td>howfairis_checklist</td> <td>Indicates if the repository has implemented OpenSSF best practices badge (True/False)</td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/tree/main/collect_variables#usage">howfairis_variable.py</a>) is a wrapper for <a href="https://pypi.org/project/howfairis/">howfairis</a> pypi library that checks the 5 recommendations of <a href="https://fair-software.nl">FAIR</a></td> </tr> <tr> <td>fair_score</td> <td>Score based on howfairis variables (0-5) </td> <td> </td> </tr> <tr> <td>dlr_soft_class</td> <td>Name of the university, company, research institute, or research organization</td> <td>(Manual) Annotated the repository based on <a href="https://core.ac.uk/reader/211557820">DLR software engineering guideline.</a> There are no specific definitions on metrics how to categorise them (github repositories) into application classes. Which were needed to do a comparitive analysis. </td> </tr> <tr> <td>installation_instruction</td> <td>Presence of installation instruction (True/False) </td> <td>(Manual) Checked the presense of Installation Instruction in the readme or in the project wiki pages. </td> </tr> <tr> <td>project_information </td> <td>Presence of basic project information in README (True/False) </td> <td>(Manual) Checked if the readme have basic information about the project. </td> </tr> <tr> <td>usage_guide</td> <td>Presence of folder named test/tests in the root directory (True/False)</td> <td>(Manual) Checked the presense of Usage Guide in the readme or in the project wiki pages. For command line tools checked if they have help command which guides how to use the tool. </td> </tr> <tr> <td>test_folder</td> <td>Presence of folder named test/tests in the root directory (True/False)</td> <td> <p>(Script - <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/docs/collect_variables/scripts/soft_dev_pract/test_folder.py">test_folder.py</a>) Checks the folder names test/tests in the root directory of the repository.</p> </td> </tr> <tr> <td>requirements_explicit </td> <td>Explicit requirements for Python, R, C++ repositories (True/False)</td> <td>(Script - <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/main/collect_variables/scripts/soft_dev_pract/requirement_explicit.py">requirement_explicit.py</a>) Checks the files (requirements.txt, DESCRIPTION, CMakeLists.txt) in the root directory. </td> </tr> <tr> <td>continuous_integration</td> <td>Indicates if the repository uses continuous integration (True/False)</td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/main/collect_variables/scripts/soft_dev_pract/continious_integration.py">continious_integration.py</a>) Checks the presence of folder .github (github actions) same for other continious integration (travisCI, CircleCI, Jekins, azure pipeline)</td> </tr> <tr> <td>ci_tool </td> <td>Name of the continuous integration tool used</td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/main/collect_variables/scripts/soft_dev_pract/continious_integration.py">continious_integration.py</a>) Checks the presence of folder .github (github actions) same for other continious integration (travisCI, CircleCI, Jekins, azure pipeline)</td> </tr> <tr> <td>add_lint_rule </td> <td>Indicates if additional linting rules are present (True/False)</td> <td>(Script - <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/main/collect_variables/scripts/soft_dev_pract/add_ci_rules.py">add_ci_rules.py</a>) - it scans the YAML files in the <br>.github/workflows directory to detect the presence of (linters) Python, R, and C++.</td> </tr> <tr> <td>add_test_rule</td> <td>Indicates if additional testing rules are present (True/False) </td> <td>(Script - <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/main/collect_variables/scripts/soft_dev_pract/add_ci_rules.py">add_ci_rules.py</a>) - it scans the YAML files in the <br>.github/workflows directory to detect the presence of (testing libraries) Python, R, and C++.</td> </tr> <tr> <td>comment_at_start</td> <td>Indicates the level of comments at the start of the program (most, more, some, less)</td> <td>(Script - <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/main/collect_variables/scripts/soft_dev_pract/comment_at_start.py">comment_at_start.py</a>) Checks the presence of brief comments at the start at source code files in GitHub repositories.</td> </tr> <tr> <td>language </td> <td>Programming language used in the repository </td> <td> </td> </tr> <tr> <td>type </td> <td>Specifies if the profile is a user or organization </td> <td>Github organisation or user profiles.</td> </tr> <tr> <td>organisation </td> <td>Name of the university, company, research institute, or research organization</td> <td>Oraganisation name (from where the user was found)</td> </tr> <tr> <td>research_group</td> <td>Name or acronym of the research group </td> <td> </td> </tr> </tbody> </table> <p> </p> <p>Data for publication - https://github.com/Software-Engineering-Group-UP/potsdam-research-repos</p>
Software repository for data-driven reconstruction of doping profiles in semiconductors
<p>Datasets and code used described in paper: "Data-driven solutions of ill-posed inverse problems arising from doping reconstruction in semiconductors" [arXiv:2208.00742]</p>
A census of research software in 171 academic institutional repositories.
<p> A dataset of metadata for 171 UK academic institutional repositories, including a census of research software contained.</p> <table> <tbody> <tr> <td><strong>URL</strong></td> <td>The OAI url</td> </tr> <tr> <td><strong>id</strong></td> <td>CORE Identifier</td> </tr> <tr> <td><strong>openDoarId</strong></td> <td>Open DOAR identifier</td> </tr> <tr> <td><strong>name</strong></td> <td>Name of repository</td> </tr> <tr> <td><strong>Russell_member</strong></td> <td>If the university is a member of the Russell Group of research intensive universities</td> </tr> <tr> <td><strong>RSE_group</strong></td> <td>If an RSE group is present (based on Soc of RSE data)</td> </tr> <tr> <td><strong>email</strong></td> <td>Redacted</td> </tr> <tr> <td><strong>uri</strong></td> <td>Not used</td> </tr> <tr> <td><strong>uni_sld</strong></td> <td>Second level domain (the part of the url between . And .ac.uk</td> </tr> <tr> <td><strong>homepageUrl</strong></td> <td>University website</td> </tr> <tr> <td><strong>source</strong></td> <td>Not used</td> </tr> <tr> <td><strong>ris_software</strong></td> <td>the Research Information System software used</td> </tr> <tr> <td><strong>ris_software_enum</strong></td> <td>Resolve ris_software into similar types (e.g. Eprints 3, EPrints3.3.16 both equal eprints)</td> </tr> <tr> <td><strong>metadataFormat</strong></td> <td>the protocol used for metadata</td> </tr> <tr> <td><strong>createdDate</strong></td> <td>Repository creation date</td> </tr> <tr> <td><strong>location</strong></td> <td>location of university</td> </tr> <tr> <td><strong>logo</strong></td> <td>University logo (resolves in error)</td> </tr> <tr> <td><strong>type</strong></td> <td>Only = Repository for this dataset. Can be = journal etc.</td> </tr> <tr> <td><strong>stats</strong></td> <td>Not used</td> </tr> <tr> <td><strong>contains_software_set</strong></td> <td>Whether the OAI-PMH software set is present in the repository.</td> </tr> <tr> <td><strong>Num_sw_records</strong></td> <td>The response of the OAI-PMH query for software (erroneous as discussed in paper)</td> </tr> <tr> <td><strong>Error</strong></td> <td>The category of error returned by the experiment’s OAI-PMH queries (see paper)</td> </tr> <tr> <td><strong>Manual_Num_sw_records</strong></td> <td>The true amount of software contained in the repository as found by a manual exhaustive search of each university website</td> </tr> <tr> <td><strong>Category</strong></td> <td>Whether the repository (a) contains software; (b) can contain software, but doesn’t yet; (c) has no separate type of research output called software or similar</td> </tr> </tbody> </table>
A dataset of metadata for UK academic institutional repositories, including a census of research software contained.
<p>A dataset of metadata for UK academic institutional repositories, including a census of research software contained.</p> <table> <tbody> <tr> <td><strong>URL</strong></td> <td>The OAI url</td> </tr> <tr> <td><strong>id</strong></td> <td>CORE Identifier</td> </tr> <tr> <td><strong>openDoarId</strong></td> <td>Open DOAR identifier</td> </tr> <tr> <td><strong>name</strong></td> <td>Name of repository</td> </tr> <tr> <td><strong>Russell_member</strong></td> <td>If the university is a member of the Russell Group of research intensive universities</td> </tr> <tr> <td><strong>RSE_group</strong></td> <td>If an RSE group is present (based on Soc of RSE data)</td> </tr> <tr> <td><strong>email</strong></td> <td>Redacted</td> </tr> <tr> <td><strong>uri</strong></td> <td>Not used</td> </tr> <tr> <td><strong>uni_sld</strong></td> <td>Second level domain (the part of the url between . And .ac.uk</td> </tr> <tr> <td><strong>homepageUrl</strong></td> <td>University website</td> </tr> <tr> <td><strong>source</strong></td> <td>Not used</td> </tr> <tr> <td><strong>ris_software</strong></td> <td>the Research Information System software used</td> </tr> <tr> <td><strong>ris_software_enum</strong></td> <td>Resolve ris_software into similar types (e.g. Eprints 3, EPrints3.3.16 both equal eprints)</td> </tr> <tr> <td><strong>metadataFormat</strong></td> <td>the protocol used for metadata</td> </tr> <tr> <td><strong>createdDate</strong></td> <td>Repository creation date</td> </tr> <tr> <td><strong>location</strong></td> <td>location of university</td> </tr> <tr> <td><strong>logo</strong></td> <td>University logo (resolves in error)</td> </tr> <tr> <td><strong>type</strong></td> <td>Only = Repository for this dataset. Can be = journal etc.</td> </tr> <tr> <td><strong>stats</strong></td> <td>Not used</td> </tr> <tr> <td><strong>contains_software_set</strong></td> <td>Whether the OAI-PMH software set is present in the repository.</td> </tr> <tr> <td><strong>Num_sw_records</strong></td> <td>The response of the OAI-PMH query for software (erroneous as discussed in paper)</td> </tr> <tr> <td><strong>Error</strong></td> <td>The category of error returned by the experiment’s OAI-PMH queries (see paper)</td> </tr> <tr> <td><strong>Manual_Num_sw_records</strong></td> <td>The true amount of software contained in the repository as found by a manual exhaustive search of each university website</td> </tr> <tr> <td><strong>Category</strong></td> <td>Whether the repository (a) contains software; (b) can contain software, but doesn’t yet; (c) has no separate type of research output called software or similar</td> </tr> </tbody> </table> <p> </p>
LabelGit: A dataset for software repositories classification using attributed dependency graphs
<p>A dataset for software repositories classification using attributed dependency graphs</p>
URLs with query strings for software in UK Academic Institutional Repositories.
<p>A set of exact URLs containing the query strings to search for software within UK Academic IRs.</p>
Paper Repository and References for "Early software defect prediction: A systematic map and review"
<p>Context: Software defect prediction is a trending research topic, and a wide variety of the published papers focus on coding phase or after. A limited number of papers, however, includes the prior (early) phases of the software development lifecycle (SDLC).<br> Objective: The goal of this study is to obtain a general view of the characteristics and usefulness of Early Software Defect Prediction (ESDP) models reported in scientific literature. <br> Method: A systematic mapping and systematic literature review study has been conducted. We searched for the studies reported between 2000 and 2016. We reviewed 52 studies and analyzed the trend and demographics, maturity of state-of-research, in-depth characteristics, success and benefits of ESDP models. <br> Results: We found that categorical models that rely on requirement and design phase metrics, and few continuous models including metrics from requirements phase are very successful. We also found that most studies reported qualitative benefits of using ESDP models.<br> Conclusion: We have highlighted the most preferred prediction methods, metrics, datasets and performance evaluation methods, as well as the addressed SDLC phases. We expect the results will be useful for software teams by guiding them to use early predictors effectively in practice, and for researchers in directing their future efforts.</p>
CVS-Vintage: A dataset of 14 CVS repositories of java software
<p>CVS-Vintage is a dataset of 14 CVS repositories of Java applications. This dataset aims at supporting the replication of early papers in the field of software evolution and mining software repositories. By building this dataset, we saved some CVS repositories from a probable death by deletion.</p> <pre>@techreport{monperrus:hal-00769121, title = {CVS-Vintage: A Dataset of 14 CVS Repositories of Java Software}, author = {Monperrus, Martin and Martinez, Matias}, url = {https://hal.archives-ouvertes.fr/hal-00769121/file/paper.pdf}, year = {2012}, number = {hal-00769121}, institution = {HAL}, }</pre> <p>A Git version is available at https://github.com/monperrus/cvs-vintage/</p>
Data supplementing the conference paper "Who you gonna call? Analyzing web requests in Android applications", 14th International Conference on Mining Software Repositories 2017.
<p>This repository contains the data supplementing the paper:</p> <p>M. Rapoport, P. Suter, E. Wittern, O. Lhótak, J. Dolby, "Who you gonna call? Analyzing web requests in Android applications", MSR 2017.</p> <p>A detailed description of the data is included in the archive in README.md.</p>
On the Use of GitHub Actions in Software Development Repositories
<p>This replication package contains all the material required to replicate the analyses we made for our paper entitled "On the Use of GitHub Actions in Software Development Repositories" accepted for publication at the 38th International Conference on Software Maintenance and Evolution (ICSME) 2022.</p> <p>The required dependencies are listed in `requirements.txt` and can be installed (preferably in a virtual environment) using `pip install -r requirements.txt`. The notebooks are expected to be executed with `jupyter lab` (but any `jupyter` environment should do the job). The data (from `data/` folders) are generated by the various notebooks, starting from the raw dataset in the `data-raw` folder. Please refer to the paper and to the README file contained in that folder for more informations.</p> <p> </p>
Data showcase papers published in the Mining Software Repositories (MSR) conference
<p>Data regarding data showcase papers published in the Mining Software Repositories (MSR) conference.</p> <p>The following data files are included.</p> <p>citation-table.csv: SWEBOK areas of citing studies<br> citations.bib: Bibliographic details of citing studies<br> citing_dp_dois_citations.txt: Citations of citing studies<br> data_papers.bib: MSR data papers<br> dp_dois_citations.txt: Citations of data papers<br> false_citations.bib: Citing studies that don't actuall use data papers<br> msr-all: Bibliographic details of all MSR papers<br> ndp_dois_citations.txt: Citations of non-data papers<br> ndp_rand_dois_citations.txt: Citations of a randomly chose non-data paper weighted sample<br> self-citations.csv: Data papers citations by their authors</p> <p> </p>
DocMine: A Software Documentation-Related Dataset of 950 GitHub Repositories
<p>DocMine dataset consists of textual information collated from multiple software artifacts, across 950 GitHub Repositories. It also consists of probable percentage contribution of text in each software artifact towards different documentation types in each repository, accompanied by metadata information about the repository such as stargazer count, number of pull requests, commits, issues and other files analyzed.</p>
How are software repositories mined? A systematic literature review of workflows, methodologies, reproducibility, and tools
<p>This is the excel spreadsheet dataset containing our analysis of papers performing mining software repositories research from the conferences ICSE, ESEC/FSE, and MSR from the years 2018 - 2020. The data is broken into columns and can be explained at a high-level as follows:</p> <p>Column Content</p> <p>1 The paper being analyzed</p> <p>2 Does the paper state the data they analyzed is available</p> <p>3 Does the paper perform some sort of data analysis or sampling using data others have compiled in the past</p> <p>4 Does the paper state a timestamp for when they begin their work</p> <p>5 Does the paper state the use of systems pre-built to help with MSR work</p> <p>6 - 18 Forms of sampling researchers may have employed to select their data</p> <p>19 What datasets (if any) were used in the analysis</p> <p>20 What tools (if any) were used in the analysis</p> <p>21 How they performed their data sampling workflow</p> <p>22 How they performed their data filtering workflow</p> <p>23 How they performed their data retrieval workflow</p> <p>24 Did they create any scripts in each of these workflows</p> <p>25 - 33 Did they publish a replication package and what is contained within</p> <p>34 Is the paper describing a tool for research or not</p> <p>35 Short description of the paper read</p> <p>36 A high-level category of the work performed in each paper</p>
How to choose a research data repository software? Experience report. Table of requirements.
<p>In the age of digital transformation, scientific and social interest for data and data products is constantly on the rise. The quantity as well as the variety of digital research data is increasing significantly. This raises the question about the governance of this data. For example, how to store the data so that it is presented transparently, freely accessible and subsequently available for re-use in the context of good scientific practice. Research data repositories provide solutions to these issues.</p> <p>Considering the variety of repository software, it is sometimes difficult to identify a fitting solution for a specific use case. For this purpose a detailed analysis of existing software is needed. Presented table of requirements can serve as a starting point and decision-making guide for choosing the most suitable for your purposes repository software. This table is dealing as a supplementary material for the paper "How to choose a research data repository software? Experience report." (persistent identifier to the paper will be added as soon as paper is published).</p>
Reproduction package for "Revisiting the reproducibility of empirical software engineering studies based on data retrieved from development repositories"
<p>Reproduction package for "Revisiting the reproducibility of empirical software engineering studies based on data retrieved from development repositories", published in Information and Software Technology, Volume 164, December 2023. DOI: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.infsof.2023.107318" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.infsof.2023.107318</span></span></a></p>
Data showcase papers published in the Mining Software Repositories (MSR) conference (v2.2)
<p>Data regarding data showcase papers published in the Mining Software Repositories (MSR) conference.</p> <p>The following data files are included.</p> <ul> <li>citing_dp_dois_citations.txt: Strong and weak citations of (strong and weak) citation papers</li> <li>data_paper_clustering.csv: The clustering process of MSR data papers</li> <li>data_paper_clusters.csv: Clusters of MSR data papers</li> <li>data_papers.bib: Bibliographic details of MSR data papers, along with their assigned clusters (field 'cluster') and strong citations (field 'usedby')</li> <li>dp_dois_citations.txt: Strong and weak citations of MSR data papers</li> <li>msr-all: Bibliographic details of all MSR (data and non-data) papers</li> <li>ndp_dois_citations.txt: Strong and weak citations of MSR non-data papers</li> <li>ndp_rand_dois_citations.txt: Strong and weak citations of a randomly chosen MSR non-data paper weighted sample</li> <li>self-citations.txt: Strong citations of MSR data papers by their authors</li> <li>strong_citation_classification.csv: The classification process of strong citation papers according to the SWEBOK knowledge areas</li> <li>strong_citation_fields.csv: SWEBOK knowledge areas of strong citation papers</li> <li>strong_citations.bib: Bibliographic details of strong citation papers</li> <li>survey_questionnaire.pdf: The final survey questionnaire</li> <li>survey_responses.csv: Anonymized responses of the final survey questionnaire (Email addresses have been excluded for privacy reasons.)</li> <li>weak_citations_notes.bib: Weak citations of MSR data papers and the use they make</li> </ul>
Implementing Traceability Repositories as Graph Databases for Software Quality Improvement: Datasets used to test our methodology that is presented in the paper 10.1109/QRS.2018.00040
<p>The first dataset is the Event Based Traceability for Managing Evolutionary Change (EBT), it is a public dataset provided by CoEST, the original artifacts and trace links are represented in XML and text format. From the EBT dataset, we selected the 41<em> requirements </em>and 25<em> test case </em>artifacts, in addition to the answer set of 51 trace links which relates the <em>requirements </em>with the<em> test case. </em>Artifacts and trace links are prepared in XML format<strong>. </strong>The data set contains XML for each artifact such as RQ.xml, EBTrelations.xml is the answer set file, TradModel.xml which describes the defined model and TradTraceabilityRule.xml that includes the rules applied for trace link types.</p> <p> </p> <p>The second dataset AgileOERP is collected from commercial management tool to customize an open source ERP applying agile methodology. It contains 350<em> user stories (US), </em>1323<em> tasks (TS) </em>and 198<em> developer test (DT) </em>artifacts, in addition to answer set of trace links that manually generated by developers which relates the <em>user story </em>artifact with<em> task (</em>1304) artifact, as such relates the <em>task </em>artifact with<em> developer test </em>artifact (65). Artifacts and trace links are prepared in XML format<strong>. </strong>The data set contains XML for each artifact such as US.xml, ERPrelations.xml is the answer set file, AgileModel.xml which describes the defined model and AgileTraceabilityRule,xml that includes all rules applied for trace links type</p> <p> </p> <p>The original dataset of the last dataset is the Aqualush irrigation system which is used as a case study in “C. Fox, Introduction to Software Engineering Design: Processes, Principles and Patterns with UML2. Addison-Wesley, 2006”. The trace links are generated and provided in “E. Ben Charrada, D. Caspar, C. Jeanneret, and M. Glinz, towards a benchmark for traceability, in Joint EVOL and IWPSE 2011, pp. 21-30”, in HTML format. For our work, we selected the <em>software requirements specification (</em>396 SRS), <em>user level requirements (</em>48 ULR), <em>use case (</em>74 UC), <em>detailed design (85 DD) </em>and <em>software architecture(15 SArch) </em>artifacts in addition to the answer set of trace links that relate the SRS with other artifacts(4038) and thus relates the DD artifact with other artifacts (1719) . Artifacts and trace links are prepared in XML<strong>. </strong>The data set contains XML for each artifact such as SRS.xml, AqualushRelations.xml is the answer set file, TradModel.xml which describes the defined model and TradTraceabilityRule.xml that includes the rules applied for trace link types.</p>
Qualisign: Software Metrics and GoF Design Patterns of the Maven Central Repository
<p>This dataset contains software metric and design pattern data for around 100,000 projects from the Maven Central repository. The data was collected and analyzed as part of my master's thesis "Mining Software Repositories for the Effects of Design Patterns on Software Quality" (https://www.overleaf.com/read/vnfhydqxmpvx, https://zenodo.org/record/4048275).</p> <p>The included qualisign.* files all contain the same data in different formats:<br> - qualisign.sql: standard SQL format (exported using "pg_dump --inserts ..."),<br> - qualisign.psql: PostgreSQL plain format (exported using "pg_dump -Fp ..."),<br> - qualisign.csql: PostgreSQL custom format (exported using "pg_dump -Fc ...").</p> <p>create-tables.sql has to be executed before importing one of the qualisign.* files. Once qualisign.*sql has been imported, create-views.sql can be executed to preprocess the data, thereby creating materialized views that are more appropriate for data analysis purposes.</p> <p>---</p> <p>Software metrics were calculated using CKJM extended:<br> http://gromit.iiar.pwr.wroc.pl/p_inf/ckjm/</p> <p>Included software metrics are (21 total):<br> - AMC: Average Method Complexity<br> - CA: Afferent Coupling<br> - CAM: Cohesion Among Methods<br> - CBM: Coupling Between Methods<br> - CBO: Coupling Between Objects<br> - CC: Cyclomatic Complexity<br> - CE: Efferent Coupling<br> - DAM: Data Access Metric<br> - DIT: Depth of Inheritance Tree<br> - IC: Inheritance Coupling<br> - LCOM: Lack of Cohesion of Methods (Chidamber and Kemerer)<br> - LCOM3: Lack of Cohesion of Methods (Constantine and Graham)<br> - LOC: Lines of Code<br> - MFA: Measure of Functional Abstraction<br> - MOA: Measure of Aggregation<br> - NOC: Number of Children<br> - NOM: Number of Methods<br> - NOP: Number of Polymorphic Methods<br> - NPM: Number of Public Methods<br> - RFC: Response for Class<br> - WMC: Weighted Methods per Class</p> <p>In the qualisign.* data, these metrics are only available on the class level. create-views.sql additionally provides averages of these metrics on the package and project levels.</p> <p>---</p> <p>Design patterns were detected using SSA:<br> https://users.encs.concordia.ca/~nikolaos/pattern_detection.html</p> <p>Included design patterns are (15 total):<br> - Adapter<br> - Bridge<br> - Chain of Responsibility<br> - Command<br> - Composite<br> - Decorator<br> - Factory Method<br> - Observer<br> - Prototype<br> - Proxy<br> - Singleton<br> - State<br> - Strategy<br> - Template Method<br> - Visitor</p> <p>---</p> <p>The code to generate the dataset is available at:<br> https://github.com/jaichberg/qualisign</p> <p>The code to perform quality analysis on the dataset is available at:<br> https://github.com/jaichberg/qualisign-analysis</p>
Mining Software Repositories for the Characterization of Continuous Integration and Delivery
<p>Continuous Integration (CI) and Delivery (CD) are software development practices increasingly adopted in industry, which aiming at ensuring quality and stability, and making the whole process more efficient and less error-prone. Software engineers that incorporate these practices may have difficulties trying to get an overview of the impact of adopting them and tracking progress. Therefore, performing analysis regarding the level of the application of CI/CD practices in a software repository can help to understand how such practices are adopted, which can result in a better characterization of their benefits and limitations. To support this analysis, we developed Garimpeiro, a web application that supports the characterization of open source repositories hosted on GitHub regarding the level of adoption of CI/CD practices. This video describes the tool and demonstrates how it can be used.<br> </p>
Extracting Enhanced Artifical Intelligence Model Metadata from Software Repositories
<p>Replication set for evaluations performed in EMSE submission "Extracting Enhanced Artifical Intelligence Model Metadata from Software Repositories" </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.
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.