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164 results for “software development”
Dataset for the study "Agile Change Approach for Collaborative Software Development Contexts" - Umbrella Review
<p>Dataset for the study "Agile Change Approach for Collaborative Software Development Contexts"</p> <p>Umbrella review - First review</p> <p>The objective of this umbrella review is to check that there are no reviews in the defined period from 2000 to 2024 that respond to the objective of this research</p> <p> </p>
Dataset for the study "Agile Change Approach for Collaborative Software Development Contexts" - Systematic Literature Review
<p>Dataset for the study "Agile Change Approach for Collaborative Software Development Contexts" </p> <p>Second review</p> <p> </p> <p>This is the dataset for the full systematic literature review</p>
Design and Development of a Smartphone-Based Geolocalized Exposure Therapy Software for Anxiety Disorders: SyMptOMS-ET -- Reproducibility Package
<p>R Notebook and datasets for the submitted paper "<em>Towards a self-applied, mobile-based geolocated exposure therapy software for anxiety disorders: SyMptOMS-ET app</em>"</p> <blockquote> <p>Alberto González-Pérez, Laura Diaz-Sanahuja, Miguel Matey-Sanz, Jorge Osma, Carlos Granell, Juana Bretón-López, Sven Casteleyn. Towards a self-applied, mobile-based geolocated exposure therapy software for anxiety disorders: SyMptOMS-ET app. <a href="https://journals.sagepub.com/home/dhj">Digital Health Journal</a> [Submitted]</p> </blockquote> <p>Experiments were conducted using the v1.2.0 version of the SyMptOMS-ET open-source app, which can be found <a href="https://github.com/GeoTecINIT/symptoms-mobile-app/releases/tag/v1.2.0">here</a>.</p>
Replication Data for "Exploring Developer Views on Software Carbon Footprint and its Potential for Automated Reduction"
<p># Replication Data for "Exploring Developer Views on Software Carbon Footprint and its Potential for Automated Reduction"</p> <p>## Overview</p> <p>Reducing software carbon footprint could contribute to efforts to avert climate change. Past research indicates that developers lack knowledge on energy consumption and carbon footprint, and existing reduction guidelines are difficult to apply. Therefore, we propose that automated reduction methods should be explored. However, such tools must be voluntarily adopted and regularly used to have an impact.</p> <p>In this study, we have conducted interviews and a survey (a) to explore developers' existing opinions, knowledge, and practices with regard to carbon footprint and energy consumption, and (b), to identify the requirements that automated reduction tools must meet to ensure adoption. Our findings offer a foundation for future research on practices, guidelines, and automated tools that address software carbon footprint.</p> <p>## Data Contained in This Package</p> <p>- interview_survey_guide.pdf</p> <p>This file contains the interview and survey questions.</p> <p>- interview_responses.docx</p> <p>This file contains relevant material from the interviews.</p> <p>- survey_responses.xlsx</p> <p>This file contains all survey responses.</p> <p>Both interview and survey data has been anonymized to protect the privacy of the participants.</p>
Dataset of article: Investigating Developers' Perception on Success Factors for Research Software Development
<p>This dataset is an addendum to the article "Investigating Developers' Perception on Success Factors for Research Software Development" to provide information regarding the anonymously collected data.</p> <p> </p> <p> </p> <p> </p>
Agile Global Software Development: A Systematic Literature Review
<p>Global Software Development (GSD) continues to grow substantially and it is fast becoming the norm and fundamentally different from local Software Engineering development. Withal, agile software development (ASD) has become an appealing choice for companies attempting to improve their performance although its methods were originally designed for small and individual teams. The current literature does not provide a cohesive picture of how the agile practices are taken into account in the distributed nature of software development: how to do it, who, and what works in practice. This study aims to highlight how ASD practices are applied in the context of GSD in order to develop a set of techniques that can be relevant in both research and practice. To answer the research question, "how are agile practices adopted in agile global software development teams?'" We conducted a systematic literature review of the ASD and GSD literature. A synthesis of solutions found in seventy-six studies provided 48 distinct practices that organizations can implement, including "collaboration among teams", "agile architecture'", "coaching", "system demo" and "test automation". These implementable practices go some way towards providing solutions to manage GSD teams, and thus to embrace agility.</p>
Code-Level Model Checking in the Software Development Workflow -- Replication Package
<p>This experience report describes a style of applying symbolic model checking developed over the course of four years at Amazon Web Services (AWS). Lessons learned are drawn from proving properties of numerous C-based systems, e.g., custom hypervisors, encryption code, boot loaders, and an IoT operating system. Using our methodology, we find that we can prove the correctness of industrial low-level C-based systems with reasonable effort and predictability. Furthermore, AWS developers are increasingly writing their own formal specifications. All proofs discussed in this paper are publicly available on GitHub. All proofs and specifications described in the paper are available, under the Apache 2.0 license, on the GitHub repository located at <a href="https://github.com/awslabs/aws-c-common/">https://github.com/awslabs/aws-c-common/</a> This is the master repository for AWS C Common library, and is in active use by the AWS C Common development team. The description of the contents of this repository are based off commit <code>b0ea9f35df8934f9e03fc3bab3919d55efd69b88</code>, although they are not expected to change significantly in the future.</p>
Dataset for the paper "Googling for Software Development: What Developers Search For and What They Find?", MSR 2021.
<p>This is the dataset for the paper "Googling for Software Development: What Developers Search For and What They Find?" submitted to the Mining Software Repositories Conference (MSR), 2021.</p> <p>This dataset has three data:</p> <ul> <li><strong>Search queries</strong>: contains the search queries to compute RQ1, RQ2, RQ3, and RQ4.</li> <li><strong>Search results RQ5</strong>: contains the search results to compute RQ5 (files starting with "search-results-rq5").</li> <li><strong>Search results RQ6</strong>: contains the search results to compute RQ6 (files starting with "search-results-rq6").</li> </ul> <p>The dataset "Search results RQ6" has 8 columns: </p> <ol> <li>same_top10: whether the top 10 links are exactly the same (0 or 1)</li> <li>same_top1: whether the top 1 links are exactly the same (0 or 1)</li> <li>inter_ratio_top5: the intersection of links in the top 5</li> <li>inter_ratio_top10: the intersection of links in the top 10</li> <li>original_query: the original queries</li> <li>original_search_resuls: the top 10 links returned for the original queries</li> <li>modified_query: the modified queries</li> <li>modified_search_resuls: the top 10 links returned for the modified queries</li> </ol> <p>Example (word swap, context)</p> <ul> <li>Original query: "java string replaceall case insensitive"</li> <li>Modified query: "string replaceall case insensitive java"</li> <li>Single row example: "0","1.0","0.8","0.9","java string replaceall case insensitive","['https://stackoverflow.com/questions/5054995/how-to-replace-case-insensitive-literal-substrings-in-java', ...]","string replaceall case insensitive java","['https://stackoverflow.com/questions/5054995/how-to-replace-case-insensitive-literal-substrings-in-java', ...]"</li> </ul>
A Case Study on the Communication of a Local Software Developer Team
<p>Raw data and scripts for the analysis of the paper "A Case Study on the Communication of a Local Software Developer Team"</p> <p>The files with the extension "list" contain the communication of the according channel. For example, "talks.list" contains all the recorded talks. The first two columns of a file contains the communication patners, the third column the date, the fourth the time of day. The sixth column contains the duration of a talk, the seventh column the rough topic, followed by the id of the event in column eight. Since often, additional developers entered the conversation, which we recorded as extra event, we have summarized the events to one conversation, denoted by the last column. That is, the last column contains the id of the conversation.</p> <p>The file analysis.Rmd contains the script that we used to analyze the data and create the plots. We used the library coronet (available at GitHub:https://github.com/ecklbarb/coronet/tree/read-data-from-company). The input data must have the folder structure as discribed in the Readme of the coronet project.</p> <p>The file analysis.Rmd must be copied in the folder of the coronet library.</p>
Supplementary Material for the Paper "Design Recommendations for Self-Monitoring in the Workplace: Studies in Software Development"
<p>Contains the supplementary material for the paper "Design Recommendations for Self-Monitoring in the Workplace: Studies in Software Development" submitted to CSCW'18. All contents are explained in the file README.txt.</p> <p>Abstract:<br> One way to improve the productivity of knowledge workers is to increase their self-awareness about productivity at work through self-monitoring. Yet, little is known about expectations of, the experience with and the impact of self-monitoring in the workplace. To address this gap, we studied software developers, as one community of knowledge workers. We used an iterative, feedback-driven development approach (N=20) and a survey (N=413) to infer design elements for workplace self-monitoring, which we then implemented as a technology probe called WorkAnalytics. We field-tested these design elements during a three-week study with software development professionals (N=43). Based on the results of the field study, we present design recommendations for self-monitoring in the workplace, such as using experience sampling to increase the awareness about work and to create richer insights, the need for a large variety of different metrics to retrospect about work, and that actionable insights, enriched with benchmarking data from co-workers, are likely needed to foster productive behavior change at work.</p>
Data, software, and Figures used in a manuscript submitted to Geoscientific Model Development
<p>Data, software, and Figures used in 'A General Comprehensive Evaluation Method for Cross-Scale Precipitation Forecast' submitted to Geoscientific Model Development.</p>
Multivocal Literature Review Protocol - Investigating the Barriers that Women face in Software Development Teams focusing on the context of Proprietary Software Ecosystems
Open the record for dataset details and reuse information.
Software Development Waste amidst COVID-19 Pandemic: An Industry Study
<p>The dataset is to support the publication "Software Development Waste amidst COVID-19 Pandemic: An Industry Study" in ISEC 2024. </p>
Data and Software of "Development of a Geometric Modeling Strategy for the Generation of Representative Unit Cells in 2D Braids"
<h1><strong>Id: Data of following publication</strong></h1> <p>title = "Development of a Geometric Modeling Strategy for the Generation of Representative Unit Cells in 2D Braids",<br>journal = "<span>Composite Structures</span>",<br>volume =" 348",<br>pages = "118503",<br>year = "2025",<br>doi = "<a href="https://doi.org/10.1016/j.compstruct.2024.118503" target="_blank" rel="noopener">10.1016/j.compstruct.2024.118503</a>",<br>author = "José Rothkegel, Benjamin Renson, Michael Bruyneel, Ludovic Noels"</p> <p>Data doi on 10.5281/zenodo.10829042</p> <h1>pyRVE</h1> <h2><em>Python Code for Geometrical Generator for Braided Composites RVE</em></h2> <p>pyRVE is a code written in <em>Python</em> using the <em>GMSH API</em> that generates the Representative Unit Cell (RUC) of braided composites. It allows the generation of the RUC of triaxial braided for <em>Diamond</em> and <em>Regular</em> patterns.</p> <h2>Requirements</h2> <p>To run, it requires:</p> <ul> <li>The GMSH Python API, which must be built with OpenCascade support. <ul> <li>Choose a local installation directory; <code>CMAKE_INSTALL_PREFIX=$HOME/local/gmsh</code>, and <code>GMSHPY_INSTALL_DIRECTORY=$HOME/local/gmsh</code> e.g.;</li> <li>Make that directory part of your <code>export PYTHONPATH=$HOME/local/gmsh/lib:$PYTHONPATH</code>.</li> <li>After compiling use <code>make install</code>.</li> </ul> </li> <li>The CM3 app dG3D if the final RVE homogenized solution is needed (<a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a>).</li> <li>Make sure that the latest version of OpenCascade (OCCT) is used. Current used version in occt-V7.8.0.</li> </ul> <h2>Usage</h2> <h3>File Structure</h3> <p>A typical run case must have a file structure, where:</p> <ul> <li><code>brd</code>: the files <code>.brd</code> and <code>.brep</code> are located here. The <code>.brd</code> is a backup of the <code>braidClass</code> instance used in the model saved using <code>pickle</code>, the <code>.brep</code> is the Boundary Representation file that can be opened with <em>GMSH</em>.</li> <li><code>csv</code>: the <code>.csv</code> file saved here is the initial output of the code. It contains the actually used dimensions and the final cover factor of the braid.</li> <li><code>data</code>: It contains <code>.csv</code> files with the material properties and the dimensions of the tows. The original model dimensions are read from here.</li> <li><code>dir</code>: In the case of running the RVE homogenization, the directions of the tow fibers are stored here. They are saved for post processing.</li> <li><code>msh</code>: the mesh file <code>.msh</code> obtained after the geometry geneartion is stores here.</li> <li><code>png</code>: in the case of automatic post processing, png files are stored here.</li> <li><code>res</code>: this folder is used to store the homogenization results. They have to be moved here.</li> <li><code>stp</code>: if acitvated, a <code>.stp</code> file of the geometry is stored here</li> <li><code>svg</code>: the projection of the geometry on the <em>x-y</em> plane is stored here.</li> <li><code>vtk</code>: A copy of the mesh file without the matrix mesh is sotred here as a `.vtk`` file.</li> </ul> <h3>How to Run</h3> <p>We will consider the current file structure to run the example in 000_Base. To run the code, it can be called from the command prompt as</p> <div> <pre><code>python3 ../../source/mainRVE.py --name <i> --pattern <pattern></code></pre> </div> <p>In this case, the <code>--name</code> refers to the index that will be given to the model, where <code><i></code> must be changed to an integer and <code>--pattern</code> refers to the wanted pattern to be used, where <code><pattern></code> must be changed to either <code>dia</code> or <code>reg</code>.</p> <blockquote> <p>Note: <code><code>--name</code>cat</code> can also be used to reproduce the regular pattern benchmark of the paper. In that case, the volume fraction of fiber in the tows is hard coded as the provided value in the reference (i.e. 0.86). For other cases, the volume fraction is evaluated from the tow cross-sections.</p> <p>Note: <code>mainRVE.py</code> must be accesible from the directory where the case is being run. This example shows the usage of the current file structure.</p> </blockquote> <h3>All Command Line Options</h3> <p>The code can be run using further options that serve different purpouses, some serving pre processing needs and other serving run administration. The different command line options are:</p> <ul> <li>Required: <ul> <li><code>--name</code> : it gives a suffix to the run model. It is usually an integer.</li> <li><code>--pattern</code> : indicates the type of pattern to be used to build the geometry. The two current options are <code>dia</code> for diamond and <code>reg</code> for regular.</li> </ul> </li> <li>Optional <ul> <li><code>-dG3D</code>: it indicates that the homogenization of the generated RUC is to be perfomed.</li> <li><code>-GMSH</code> : it indicates that GMSH must be open upon competion of the generation of the mesh.</li> <li><code>-loadModel</code> : it will try to load a premade model. It will ignore <code>--pattern</code>.</li> <li><code>--rndPrm</code> : it will generate randomized geometrical parameters. It can be used to generate batches of results. It takes an argument that can be <code>2</code>, <code>4</code> or <code>6</code>. Currently, <code>2</code> gives a random value for <code>s_axial</code> and <code>theta</code>, <code>4</code> randomizes the same as <code>2</code> and adds <code>h_axial</code> and <code>h_bias</code>, and <code>6</code> randomizes the same as <code>4</code> and adds <code>w_axial</code> and <code>w_bias</code>.</li> </ul> </li> <li>Pre-Processing <ul> <li><code>-refCF</code>: it tells the code to generate a grid of values for <code>s_axial</code> and <code>theta</code> where only the cover factor is obtained. It is meant for posterior graphing purposes.</li> </ul> </li> </ul> <h3>Examples</h3> <p>Following the run options, a few examples are indicated</p> <ul> <li>A basic mesh generation run for the basic data, considering a <strong>regular pattern</strong>, for a model named <strong>2</strong>:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 --pattern reg</code></pre> </div> <ul> <li>The generation of the cover factor data and export, considering a <strong>regular pattern</strong>:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --pattern reg -refCF</code></pre> </div> <ul> <li>A run for the modified basic data, where the <strong>2</strong> parameters are modified <em>randomly</em>, considering a <strong>regular pattern</strong>, for a model named <strong>2</strong>:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 --pattern reg --rndPrm 2</code></pre> </div> <ul> <li>A run, where model <strong>2</strong> already exists in <code>brd</code> folder but not the <code>.msh</code> and <code>.vtk</code> files:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 -loadModel </code></pre> </div> <h2>Code Structure</h2> <p>The code is implemented into Python files, where <code>mainRVE.py</code> runs the whole code. The files are:</p> <ul> <li>Braid: <ul> <li><code>braidClass.py</code> :</li> <li><code>bzrPairClass.py</code> :</li> </ul> </li> <li>Geometry <ul> <li><code>bezrClass.py</code> :</li> <li><code>bilnClass.py</code> :</li> <li><code>patchClass.py</code> :</li> <li><code>pntSetClass.py</code> :</li> <li><code>pointClass.py</code> :</li> <li><code>sctnClass.py</code> :</li> <li><code>stripeClass.py</code> :</li> <li><code>surfClass.py</code> :</li> <li><code>surfOffClass.py</code> :</li> </ul> </li> <li>Material: <ul> <li><code>chamis.py</code> :</li> </ul> </li> <li>Tools: <ul> <li><code>dataIO.py</code> :</li> <li><code>postDirection.py</code> :</li> <li><code>tool.py</code> :</li> <li><code>toolData.py</code> :</li> </ul> </li> <li><code>curveClass.py</code> :*</li> </ul> <p> </p> <p> </p>
Replication Package: Pandemic Startup Software Engineering: An Experience Report on the Development of a COVID-19 Certificate Verification System
<p><strong>Welcome to the public repository for the additional content of the paper "Pandemic Startup Software Engineering: An Experience Report on the Development of a COVID-19 Certificate Verification System" (Journal of Systems and Software)<br></strong></p> <p>This repository provides additional information to the experience report, including the following files:</p> <ul> <li>survey_questions_de.txt: sheet containing the online questionnaire in German (original language)</li> <li>survey_questions_en.txt: sheet containing the online questionnaire translated into English</li> <li>survey_answers_original.csv: sheet containing the extracted questionnaire data of the participants in German (original language)</li> <li>survey_analysis.csv: sheet containing the analysis of the extracted questionnaire data in English</li> </ul>
CodePori: Large Scale System for Autonomous Software Development by Using Multi-Agents
<p>This dataset accompanies the paper <strong>"CodePori: A Large-Scale System for Autonomous Software Development Using Multi-Agents."</strong> The dataset is recorded in an MS Excel file, which contains the following sheets, with a brief description of each provided below:</p> <ol> <li> <p><strong>Selected Projects:</strong> Contains the descriptions of the 20 selected projects along with the GitHub URL for each.</p> </li> <li> <p><strong>Modifications:</strong> Contains details of the modifications made to the projects to ensure successful execution.</p> </li> <li> <p><strong>Outputs:</strong> Contains the output for each project.</p> </li> <li> <p><strong>Failed Projects:</strong> Contains data on the projects that failed.</p> </li> </ol>
Supporting Data for pyCSEP: A Software Toolkit for Earthquake Forecast Developers
<p>Contains data needed to reproduce the figures from the publication of pyCSEP: A Software Toolkit for Earthquake Forecast Developers.</p> <p><br> evaluation_catalog.json<br> evaluation_catalog_zechar2013_merge.txt<br> SRL_2018031_esupp_Table_S1.txt<br> <br> bird_liu.neokinema-fromXML.dat<br> ebel.aftershock.corrected-fromXML.dat<br> helmstetter_et_al.hkj.aftershock-fromXML.dat<br> lombardi.DBM.italy.5yr.2010-01-01.dat<br> meletti.MPS04.italy.5yr.2010-01-01.dat<br> werner.HiResSmoSeis-m1.italy.5yr.2010-01-01.dat<br> <br> config.json<br> m71_event.json<br> results_complete.bin</p>
Using social media and personality traits to assess software developers' emotions
<p>Companion DATA of the paper "Using social media and personality traits to assess software developers’ emotions" submitted to the IEEE Access journal, 2022.</p> <p>The folders contain:</p> <p><br> /analysis<br> analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists<br> analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants<br> analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications</p> <p><br> /dataset<br> alldata.json: contains the dataset used in the paper</p> <p><br> /notebooks<br> General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper<br> Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study<br> Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results</p> <p> Statistics - Polynomial Regression: notebook file with the polynomial regression results<br> Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis<br> Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period</p> <p><br> /surveys<br> Demographic_Survey.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts<br> Demographic_Survey_answers.xlsx: participants' demographic survey answers<br> ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits<br> ibf_answers.xlsx: participantes' and psychologists' answers for BFI</p> <p><br> ------------------------------------------------------------</p> <p><br> We have removed from dataset any sensible data to protect participants' privacy and anonymity.<br> We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.</p>
Study Data: Semi-Automated Prioritization of Industrial Security Findings in Agile Software Development
<p>Dataset for the study of the paper "Semi-Automated Prioritization of Industrial Security Findings in Agile Software Development"</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>
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.