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18 results for “Software teams”
ePol: Impacts of Diversity on Software Teams Dataset
<p>Data set that has all the records of the activities carried out in the ePol Project, with their respective attributes. Additionally, it also includes code that does a cleanup according to some criteria. After the cleaning performed by the aforementioned code, the results are also available in the "dataset_epol_result.csv" file.</p> <p>For more information read: SOUZA, NATAN. MASSONI, TIAGO. SARMENTO, CAMILLA. <strong>Impactos da Diversidade em Equipes de Software</strong>, 2023.</p>
Rapid Review Dataset for Seeking Enlightenment: Incorporating Evidence-Based Practice Techniques in a Research Software Engineering Team
<p>A collection of evidence briefings produced through a rapid literature review protocol the Department of Software Engineering and Research at Sandia National Laboratories. These briefings are described in our research paper, "Seeking Enlightenment: Incorporating Evidence-Based Practice Techniques in a Research Software Engineering Team", which was accepted for publication at the 1st Annual Conference of the United States Research Software Engineer Association (US-RSE'23).</p> <p>Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. SAND2023-06549O.</p>
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>
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.
The Double-Edged Sword Of Diversity: How Diversity, Conflict, and Psychological Safety Impacts Software Teams: Dataset and Supplementary Materials
<p>This bundle contains supplementary materials for an upcoming academic publication The Double-Edged Sword Of Diversity: How Diversity, Conflict, and Psychological Safety Impacts Software Teams, by Christiaan Verwijs and Daniel Russo. Included in the bundle are the dataset, SPSS syntaxes, and model definitions (AMOS). This replication package is made available by C. Verwijs under a "Creative Commons Attribution Non-Commercial Share-Alike 4.0 International"-license (CC-BY-NC-SA 4.0).</p><p><strong>About the dataset</strong></p><p>The dataset (SPSS) contains anonymized response data from 1.118 respondents aggregated into 161 (Agile) software teams that participated from the <a href="https://scrumteamsurvey.org">https://scrumteamsurvey.org</a>. Data was gathered between September 2021, and January 2022. We cleaned the individual response data from careless responses and removed all data that could potentially identify teams, individuals, or their parent organizations. Because we wanted to analyze our measures at the team level, we calculated a team-level mean for each item in the survey. Such aggregation is only justified when at least 10% of the variance exists at the team level (Hair, 2019), which was the case (ICC = 35-45%). No data was missing on the team level.</p><p>The dataset contains question labels and answer option definitions. To conform to the privacy statement of [scrumteamsurvey.org](https://scrumteamsurvey.org), the bundle does not include response data from before the team-level aggregation.</p><p><strong>About the model definitions</strong></p><p>The bundle includes definitions for Structural Equation Models (SEM) for AMOS. We added the iterations of the measurement model, four models used to perform a test for common method bias, and the path model that includes interactions. Indirect effects were calculated with the <a href="http://statwiki.gaskination.com/index.php?title=CFA">"Indirect Effects" plugin</a> for AMOS by James Gaskin.</p><p><strong>About the SPSS syntaxes</strong></p><p>The bundle includes the syntaxes we used to prepare the dataset from the raw import, as well as the syntax we used to generate descriptives. This is mostly there for other researchers to verify our procedure.</p>
Refactoring Dataset for SOEN 6491 Software Refactoring, Winter 2024 - Team Alpha
<p>The dataset collected by Team Alpha in SOEN 6491 Software Refactoring course, Winter 2024</p> <p><strong>Content Description</strong></p> <table> <tbody> <tr> <td><strong>File name</strong></td> <td><strong>File type</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>vertx.db, spring-boot.db, mina-sshd.db, guava.db</td> <td>SQLite Database</td> <td> <p>Fully collected refactoring dataset:</p> <ul> <li>Refactoring data (via RefactoringMiner)</li> <li>Code smells (via Organic)</li> <li>Code duplications (via PMD)</li> <li>Code churn (via Git)</li> <li>Timeline and statistics of releases in relation to refactoring commits. Releases are collected via GitHub API.</li> </ul> </td> </tr> <tr> <td>flink.db, rocketmq.db, ant.db, dubbo.db, elasticsearch.db, neo4j.db, rxjava.db, zookeeper.db</td> <td>SQLite Database</td> <td> <p>Partially collected refactoring dataset:</p> <ul> <li>Refactoring data (via RefactoringMiner)</li> </ul> </td> </tr> </tbody> </table>
Supplementary Material for "Investigating Software Development Teams Members' Perceptions of Data Privacy in the Use of Large Language Models (LLMs)"
<h3>ABSTRACT<strong>: </strong></h3> <p><strong>Context</strong>: Large Language Models (LLMs) have revolutionized natural language generation and understanding. However, they raise significant data privacy concerns, especially when sensitive data is processed and stored by third parties. <br><strong>Goal</strong>: This paper investigates the perception of software development teams members regarding data privacy when using LLMs in their professional activities. Additionally, we examine the challenges faced and the practices adopted by these practitioners. <br><strong>Method</strong>: We conducted a survey with 78 ICT practitioners from five regions of the country. <br><strong>Results</strong>: Software development teams members have basic knowledge about data privacy and LGPD, but most have never received formal training on LLMs and possess only basic knowledge about them. Their main concerns include the leakage of sensitive data and the misuse of personal data. To mitigate risks, they avoid using sensitive data and implement anonymization techniques. The primary challenges practitioners face are ensuring transparency in the use of LLMs and minimizing data collection. Software development teams members consider current legislation inadequate for protecting data privacy in the context of LLM use. <br><strong>Conclusions</strong>: The results reveal a need to improve knowledge and practices related to data privacy in the context of LLM use. According to software development teams members, organizations need to invest in training, develop new tools, and adopt more robust policies to protect user data privacy. They advocate for a multifaceted approach that combines education, technology, and regulation to ensure the safe and responsible use of LLMs.</p>
Data from: Co-creation in fully remote software teams
<p>The dataset consists of the interview protocols and codebook used in the design and data analysis steps of our study entitled "Co-Creation in Fully Remote Software Teams".</p> <p>The interview-based study consisted of a first round of interviews with 25 software professionals with a subsequent follow-up interview with 5 of these original participants. The interview transcripts were coded inductively resulting in the final code book shared here.</p>
Data from: Co-creation in fully remote software teams
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Dataset for the article: A Family of Experiments to Study how Personality may Affect Software Development Effectiveness in Teams
<p>Raw Data of the article named "A Family of Experiments to Study how Personality may Affect Software Development Effectiveness in Teams"</p>
Transformed raw data and R scripts for the paper Problem reports and team maturity in agile automotive software development published at CHASE2022
<p>Transformed raw data and R scripts for the paper Problem reports and team maturity in agile automotive software development published at CHASE2022</p> <p>15th International Conference on Cooperative and Human Aspects of Software Engineering, May 21–22, 2022, Pittsburgh, PA, USA}´</p>
"Software is the easy part of Software Engineering" - Lessons and Experiences from A Large-Scale, Multi-Team Capstone Course
<p>Our data contains the peer review form that students used to assess each member in a sub-team. Additionally, we included the project description for the capstone project. Please note that our domain experts provided additional in person requirements that students elicited for the project. However, this project description can serve as a good starting point for determining the primary project criteria. Finally, we include a sample software requirements specification that we provided for our students to use for their initial requirements specification. </p>
Online Appendix of the Paper "The Power of Words in Agile vs. Waterfall Development: Written Communication in Hybrid Software Teams"
<h2>GENERAL INFORMATION</h2> <div>In our publication “<em><strong><a href="https://doi.org/10.1016/j.jss.2024.112243">The Power of Words in Agile vs. Waterfall Development: Written Communication in Hybrid Software Teams</a>,”</strong></em> in the Journal of Systems and Software, we present an exploratory case study conducted in a large software organization, AFAS Software. Our study investigates the influence of the development paradigm and the formality of communication channels on written communication within hybrid development teams. Our main research question is: <em>“<strong>How do the employed development paradigm and the formality of the communication channel impact the written communication content within hybrid software development teams?”</strong> </em>To address the main research question, we operationalize communication content through a coding scheme comprising codes derived from the Project Management Life Cycle (PMLC), the Software Development Life Cycle (SDLC), and speech acts types, which leads to the following sub-research questions:</div> <ul> <li><em>How do the employed development paradigm and the formality of the communication channel impact </em><em>the content of written communication pertaining to the Project Management Life Cycle phases? </em><em>(RQ1A & RQ1B)</em></li> <li><em>How do the employed development paradigm and the formality of the communication channel impact </em><em>the content of written communication pertaining to the Software Development Life Cycle phases? </em><em>(RQ2A & RQ2B)</em></li> <li><em>How do the employed development paradigm and the formality of the communication channel impact </em><em>the use of speech acts types in written communication? (RQ3A & RQ3B)</em></li> </ul> <div>To answer these research questions, we test the following hypotheses regarding the PMLC, the SDLC, and speech acts types:</div> <div> </div> <div><em><strong>The content of written communication within a development team</strong></em></div> <div> <ul> <li><strong>H1</strong>: is affected by the development paradigm. <ul> <li><strong>H1.1</strong>: in formal communication channels is affected by the development paradigm.</li> <li><strong>H1.2:</strong> in informal communication channels is affected by the development paradigm.</li> </ul> </li> </ul> </div> <div><em><strong>The content of written communication within</strong></em></div> <div> <ul> <li><strong>H2:</strong> a development team is affected by the formality of the communication channel. <ul> <li><strong>H2.1:</strong> a non-agile development team is affected by the formality of the communication channel.</li> <li><strong>H2.2:</strong> an agile development team is affected by the formality of the communication channel.</li> </ul> </li> </ul> </div> <div>This online appendix contains supplementary material to uphold transparency and facilitate the reproduction of the statistical analysis. For more information, refer to the publication:</div> <ul> <li>Section 2.5 for the projects sampling</li> <li>Section 2.6 for data extraction and preparation</li> <li>Section 2.7 for the coding scheme and data analysis</li> </ul> <p><strong>Software Dependencies: </strong></p> <ul> <li>PDF Viewer (e.g. Acrobat Reader)</li> <li>IBM SPSS</li> <li>Microsoft Excel</li> <li>Python</li> </ul> <h2>FILES OVERVIEW</h2> <h3>JSS Online Appendix</h3> <div>The root folder includes the <strong>“Project Teams Composition.pdf</strong><em><strong>”</strong></em> file, which contains the anonymized compositions of 20 project teams: 11 agile (PrAG) projects and 9 waterfall (PrWF) projects. This file lists the project team members involved in communication within the Microsoft Teams and Insite channels. </div> <h3>Subfolder “SPSS Files”</h3> <div>This subfolder includes the SPSS files, which contain the normality test and Mann-Whitney U Tests. </div> <div> <ul> <li><strong>Normality Test.spv</strong>: This file contains the results of the normality test.</li> </ul> </div> <div>We performed the normality test. In most cases, the significance of Shapiro-Wilk is below 0.05; thus, the data is not normally distributed and fails to meet the assumption for the t-test. We, therefore, opted for the Mann-Whitney U test, the non-parametric alternative of the t-test.</div> <div> </div> <div>The files below contain the results of Mann-Whitney U Tests, which are presented in Appendix A - Tables A.7 (a), A.8 (a), A.9 (a):</div> <ul> <li><strong>H1 - PrAG vs PrWF Product.spv</strong>: This file contains the results for hypothesis 1. </li> <li><strong>H1.1 - PrAG vs PrWF Formal.spv</strong>: This file contains the results for hypothesis 1.1.</li> <li><strong>H1.2 - PrAG vs PrWF Informal.spv</strong>: This file contains the results for hypothesis 1.2.</li> </ul> <div>The files below contain the results of Mann-Whitney U Tests, which are presented in Appendix A - Tables A.7 (b), A.8 (b), A.9 (b):</div> <ul> <li><strong>H2 - PrAG vs PrWF Formality.spv</strong>: This file contains the results for hypothesis 2.</li> <li><strong>H2.1 - PrWF Formal vs Informal.spv</strong>: This file contains the results for hypothesis 2.1.</li> <li><strong>H2.2 - PrAG Formal vs Informal.spv</strong>: This file contains the results for hypothesis 2.2.</li> </ul> <h3>Subfolder “Excel Files”</h3> <div>This subfolder includes the necessary files to perform calculations and the results of these calculations.</div> <ul> <li><strong>JSS Paper.xlsx</strong>: This file contains the absolute numbers, which are used as input for the calculations.</li> <li><strong>JSS-Statistics.py</strong>: This Python script uses the <strong>“</strong><strong>JSS Paper. xlsx<em>”</em></strong><em><strong> </strong></em>file to perform calculations based on the hypotheses for Project Life Cycle Management (PMLC), Software Development Life Cycle (SDLC), and Speech Acts.</li> </ul> <div>The files below contain the results of the calculations:</div> <ul> <li><strong>PMLC (RQ1A & RQ1B)_v1.xlsx:</strong> This file contains the results for the PMLC phases. The results are presented in Appendix A.1.</li> <li><strong>SDLC (RQ2A & RQ2B)_v1.xlsx:</strong> This file contains the results for the SDLC phases. The results are presented in Appendix A.2.</li> <li><strong>Speech Acts (RQ3A & RQ3B)_v1.xlsx:</strong> This file contains the results for the speech act types. The results are presented in Appendix A.3.</li> </ul> <h2>DATA ACCESS AND SHARING</h2> <div><strong>Recommended citation for this dataset:</strong> Ly, D., Overeem, M., Brinkkemper, S., Dalpiaz, F., 2024. Online Appendix of the Paper “The Power of Words in Agile vs. Waterfall Development: Written Communication in Hybrid Software Teams”. [Data set]. In The Journal of Systems and Software. Zenodo. doi: 10.5281/zenodo.13894086.</div> <div> </div> <div><strong>License information:</strong><em><strong> </strong></em>Creative Commons Attribution 4.0 International</div>
Supporting Students in Team-Based Software Development Projects: An Exploratory Study - Dataset
<p>Dataset for the paper "Supporting Students in Team-Based Software Development Projects: An Exploratory Study"</p>
Supplemental Material for "Objectives and Key Results in Software Teams: Challenges, Opportunities and Impact on Development"
<p>This package contains the research materials that were used for the paper "Objectives and Key Results in Software Teams: Challenges, Opportunities and Impact on Development".</p> <ul> <li><strong>Survey on Goals and Objectives.docx</strong> contains the survey instrument that was used for the study.</li> </ul>
PSAS: A Framework for Peer Assessment of an Individuals Skills in a Software Projects Team
<p>Competency assessment in a software project team allows you to identify how each individual can collaborate to achieve the success of a project. The peer assessment approach can bring better results by having the vision of more than one team member. This article proposes a framework to automate the peer assessment of people’s competences in software project teams, developed and evaluated using the interaction design approach. The framework allows to create and apply peer reviews with co-assessment, 360-degree and self-assessment strategies and graphically analyze the performance of people, teams, compare the results of the assessment, identify highlights and follow trends through history.</p>
Replication package for the paper - "Balanced Knowledge Distribution among Software Development Teams - Observations from Open-Source and Closed-Source Software Development"
<p>Replication package for the paper - "Balanced Knowledge Distribution among Software Development Teams - Observations from Open-Source and Closed-Source Software Development".</p>
Salutare One Referral Software Impact on Multi-Disciplinary Team (MDT) Meetings Effectiveness and Safety
ClinicalTrials.gov study NCT07104955. IPD Sharing: YES. Countries: 1. Publications: 0.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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