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23 results for “User story”
Viva con Agua de St. Pauli e.V. User Stories created by socio-trechnical walkthroughs
<p>In March 2016 six socio-technical walktroughs (STWT, Hermann et al. 2004) have been conducted to identify requirements for the technical tool Pool used by Viva con Agua de St. Pauli e.V. (VCA) as a socio-technical organization (Kunau 2006). Volunteers of the NGO, as well as employees and partners were invited to participate one weekend for one STWT workshop. Every workshop has covered two days (a weekend) and focused one specific business process of VCA. The participants were independently selected for each working procedure that became a topic for a workshop.</p> <p>During the workshops, the existing business processes have been identified in a first step and during a second one improved. Afterwards, the moderator of the workshops has derived user stories for new technical support functions needed to establish the reworked business processes. These user stories are collected in the user stories catalog that is published in this data publication.</p> <table align="left"> <tbody> <tr> <td>(Herrmann et al. 2004)</td> <td>Herrmann, T., Kunau, G., Loser, K.-U., and Menold, N. Socio-technical walkthrough: Designing technology along work processes. <em>Proceedings of the eighth conference on Participatory design Artful integration: interweaving media, materials and practices - PDC 04</em>, ACM Press (2004), 132.</td> </tr> <tr> <td>(Kunau 2006)</td> <td>Kunau, G. Facilitating computer supported cooperative work with socio-technical self-descriptions. Technische Universität Dortmund, (2006).</td> </tr> </tbody> </table> <p> </p>
User Stories made by Users Workshop Analysis Results
<p>In order to enable members of a socio-technical evolutionary-teal organization to design their technical component, we conducted a workshop that structures the collaboration between technical trained participants and non-trained participants. The workshop aims to transform "vague needs" into technical descriptions in the form of user stories.</p> <p>The workshop is the second part of series of workshops all limited to two hours. It uses the methods of <em>Design Thinking</em> and <em>Participatory Design</em>.</p> <p>The workshop has been recorded in video and the resulting data set has been published on Zenodo:</p> <p>Sell, Johann, & John, Elias. (2020). User Stories made by Users Workshop Data Set (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3898358</p> <p>A qualitative analyzes has been conducted covering four iterations of coding. This data set shows the results of last iteration and the resulting insights are referenced by a research paper that is currently under review.</p> <p>We hope that the material can be used to (a) comprehend the interpretation used in our qualitative research, and to (b) investigate other interesting research questions.</p>
User stories and xAPI statements for "A mobile campus application as a sensor node for Personal Learning Environments"
<p>This dataset provides the full user stories and xAPI statements as used in the prototype described in the article "A mobile campus application as a sensor node for Personal Learning Environments". It consists of two PDF documents described below. The files were created as part of the master thesis of Hendrik Geßner.</p> <p>"User Stories.pdf" contains a complete list of user stories with required context information, existing portlets and a category. The process that led to this collection is described very briefly in the article mentioned above, a graphical explanation is available in the attached image "Use case process complete.jpg"</p> <p>"xAPI Statements.pdf" contains all xAPI statements used in the prototype described in the article mentioned above. Dynamic elements such as names or IDs are highlighted on color. The statements appear in the following order: attended, used, loggedin, wasat, opened, closed, joined, left.</p>
CERN Analysis Preservation User Stories
<p>This comic-like drawing is a graphical representation of the User Stories identified for CERN Analysis Preservation.</p>
Data and results for the paper: "From Bugs to Benefits: Improving User Stories by Leveraging Crowd Knowledge with CrUISE-AC"
<div> <div>We provide the following files used in the study "From Bugs to Benefits: Improving User Stories by Leveraging Crowd Knowledge with CrUISE-AC".</div> <div>The paper has been accepted for presentation in the research track of the IEEE/ACM International Conference on Software Engineering (ICSE) 2025 and will be included in the conference proceedings.</div> <div>The preprint is available on <a href="http://arxiv.org/abs/2501.15181" target="_blank" rel="noopener">arXiv</a>.</div> <br> <div><strong>User stories e-commerce.xlsx</strong></div> <div>307 real-world user stories from 3 different eCommerce projects.</div> <br> <div><em>Project A</em> defines a complete set of requirements for a B2C focused onlineshop of a publishing house who aims do sell his own publications directly.</div> <div><em>Project B</em> contains a partial set of requirements for a B2C focused onlineshop of a bookseller.</div> <div><em>Project C</em> includes a subset of B2C and B2B requirements for an online bookstore, supplemented by an eProcurement module designed to provide information and automation for industrial customers.</div> <div>Most of the user stories come with additional acceptance criteria, written in unstructured natural language.</div> <br> <div>The user stories have been anonymized and the merchant's real names were replaced with neutral terms.</div> <br> <div>Columns</div> <div>- ID: a unique ID we assigned across all projects</div> <div>- Project: user story belongs to project A, B or C</div> <div>- Connextra: user story in connextra pattern</div> <div>- Acceptance Criteria: acceptance criteria that came with the user story</div> <br> <div><strong>User stories CMS.xlsx</strong></div> <div>34 CMS related user stories from a dataset that was originally created by</div> <div>*Lucassen, G., Dalpiaz, F., van der Werf, J.M.E., Brinkkemper, S.: Visualizing user</div> <div>story requirements at multiple granularity levels via semantic relatedness. In: Con-</div> <div>ceptual Modeling: 35th International Conference, ER 2016, Gifu, Japan, November</div> <div>14-17, 2016, Proceedings 35. pp. 463–478. Springer (2016)*</div> <br> <div>Columns</div> <div>- ID: a unique ID we assigned</div> <div>- Connextra: user story in connextra pattern</div> <br> <div><strong>Issues e-commerce.xlsx</strong></div> <div>54,396 issues, we harvested from seven different issue trackers between June 2011 and July 2024</div> <div>- magento2 (https://github.com/magento/magento2/issues)</div> <div>- nopCommerce (https://github.com/nopSolutions/nopCommerce/issues)</div> <div>- OpenCart (https://github.com/opencart/opencart/issues)</div> <div>- PrestaShop (https://github.com/PrestaShop/PrestaShop/issues)</div> <div>- Shopware5 (https://issues.shopware.com/?products=SW-5)</div> <div>- Shopware6 (https://issues.shopware.com/?products=SW-6)</div> <div>- WooCommerce (https://github.com/woocommerce/woocommerce/issues)</div> <br> <div>Columns</div> <div>- id: unique ID we have assigned</div> <div>- Issue Tracker: issue tracker this issue originates from</div> <div>- Title: title of the original issue</div> <div>- Body: body / description of the original issue</div> <div>- Preprocessed: result of preprocessing the issue as described in the paper</div> <div>- Sample: issue was part of our 3,500 sample issues we used to evaluate CrUISE-AC</div> <br> <div><strong>Issues CMS.xlsx</strong></div> <div>64,500 issues, we harvested from two different issue trackers between April 2002 and August 2024</div> <div>- Moodle (https://github.com/magento/magento2/issues)</div> <div>- Umbraco (https://github.com/nopSolutions/nopCommerce/issues)</div> <br> <div>Columns are the the same as for "Issues e-commerce.xlsx"</div> <br> <div><strong>trivia-trainingdata.csv</strong></div> Manually labelled dataset to train the trivia classifier. <div>The dataset contains 1916 phrases with an even distribution of 958 trivia and 958 non-trivia phrases.</div> <br> <div>- Label = 1: this sentence is trivia</div> <div>- Label = 0: this sentence is not considered trivia</div> <br> <div>Any source code was replaced by [CODE] to simplify the classification process. Source code in markdown could be identified easily as it is enclosed by a special character https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/creating-and-highlighting-code-blocks</div> <br><strong>Prompts</strong><br> <div><em>prompt_match.txt</em>: prompt we used across all LLMs to assess, if an issue potentially might affect a given user story</div> <em>prompt_generate.txt</em>: GPT4-turbo prompt to convert an issue text into gherkin-style acceptance criteria for a given user story<br> <div><em>prompt_evaluate.txt</em>: GPT4-turbo prompt to assess the usefulness of a newly generated acceptance criteria for a given user story</div> <br> <div><strong>Evaluation e-commerce.xlsx</strong></div> issue / user story pairs, generated acceptance criteria and result of manual evaluation.<br> <div> </div> <div>Columns</div> <div>- StoryID: unique ID of the user story (refer to User stories e-commerce.xlsx)</div> <div>- IssueID: unique ID of the issue (refer to Issues e-commerce.xlsx)</div> <div>- Issue: preprocessed issue text used as basis to generate the acceptance criterion</div> <div>- Connextra: user story in connextra pattern</div> <div>- Existing AC: acceptance criteria that originally came with the user story</div> <div>- AC: by CrUISE-AC generated acceptance criterion</div> <div>- AC_Explanation: explanation generated by CrUISE-AC why this AC adds new knowledge to the current user story</div> <div>- E1: evaluation result by expert 1 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E2: evaluation result by expert 2 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E3: evaluation result by expert 3 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E4: evaluation result by expert 4 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- 3/4 majority: did at least 3 experts assess this AC as relevant (1 = yes; 0 = no)</div> <br> <div><strong>Evaluation CMS.xlsx</strong></div> <div>- StoryID: unique ID of the user story (refer to User stories CMS.xlsx)</div> <div>- IssueID: unique ID of the issue (refer to Issues CMS.xlsx)</div> <div>- Issue: preprocessed issue text used as basis to generate the acceptance criterion</div> <div>- Connextra: user story in connextra pattern</div> <div>- AC: by CrUISE-AC generated acceptance criterion</div> <div>- AC_Explanation: explanation generated by CrUISE-AC why this AC adds new knowledge to the current user story</div> <div>- E1: evaluation result by expert 1 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E4: evaluation result by expert 4 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E5: evaluation result by expert 5 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- 2/3 majority: did at least 2 experts assess this AC as relevant (1 = yes; 0 = no)</div> </div>
A User Story Dataset for Library and Restaurant Management Systems
<p>A user story dataset generated by participants during user experiments with <a href="https://doi.org/10.5281/zenodo.7529090">SCOUT</a>. </p>
Paper Prototype - Example of LearnIn's Personal Learning Record Store as User Story
<p>This artefact illustrates a possible user journey and very simplified, the support that LearnIn's digital ecosystem can provide. <br> The steps and stages show - in a paper prototype style - MIZO's learning pathway. </p>
US4USec: A User Story Model for Usable Security
<p>Excel sheet containing information used for the construction of the US4USec: A User Story Model for Usable Security </p>
Automatic User Story Generation: A Comprehensive Systematic Literature Review - Data Extraction
<p>This document presents the data extraction performed for the Systematic Literature Review in Automatic User Story Generation.</p>
Data for: Evaluation of Methods for Eliciting and Specifying Usability Requirements using User Stories: A Controlled Experiment
<p>Este projeto contém os materiais utilizados na pesquisa intitulada Evaluation of Methods for Eliciting and Specifying Usability Requirements using User Stories: A Controlled Experiment: TCLE, Formulário de Caracterização, Cenário, Oráculo, User Stories, Protótipo, Storyboards, Avaliação de ferramentas em escala de Likert e Dados coletados do formulário.</p>
User stories for eCommerce systems
<p>This datasets provides 307 real-world user stories from 3 different eCommerce projects.</p> <ul> <li><strong>Project A</strong> defines a complete set of requirements for a B2C focused onlineshop of a publishing house who aims do sell his own publications directly.</li> <li><strong>Project B</strong> contains a partial set of requirements for a B2C focused onlineshop of a bookseller.</li> <li><strong>Project C</strong> includes a subset of B2C and B2B requirements for an online bookstore, supplemented by an eProcurement module designed to provide information and automation for industrial customers.</li> </ul> <p>Most of the user stories come with additional acceptance criteria, written in unstructured natural language.</p> <p>The user stories have been anonymized and the merchant's real names were replaced with neutral terms.</p> <p>This dataset has been used in our paper "From Bugs to Benefits: Improving User Stories by Leveraging Crowd Knowledge with CrUISE-AC" which has been accepted for presentation in the research track of the IEEE/ACM International Conference on Software Engineering (ICSE) 2025 and will be included in the conference proceedings.</p> <div>The preprint of the paper is available on <a href="http://arxiv.org/abs/2501.15181" target="_blank" rel="noopener">arXiv</a>.</div>
Dataset for research paper "An Impact-Driven Approach to Predict User Stories Instability"
<p>Dump of all the data from an SQL server containing all the data used for the research in the research paper "An Impact-Driven Approach to Predict User Stories Instability", which is currently under review for Requirements Engineering journal. </p>
FREYA Ambassadors' Webinar - PIDs in use: collecting user stories
<p>Webinar outline</p> <ol> <li> <p><strong>Welcome and introduction FREYA team </strong></p> </li> <li> <p><strong>Go through structure of the webinar </strong></p> </li> <li> <p><strong>Introduction of what a user story is</strong></p> </li> <li> <p><strong>Discussion:</strong></p> </li> </ol> <p><strong> </strong></p>
User Stories made by Users Workshop Data Set
<p>In order to enable members of a socio-technical evolutionary-teal organization to design their technical component, we conducted a workshop that structures the collaboration between technical trained participants and non-trained participants. The workshop aims to transform "vague needs" into technical descriptions in the form of user stories.</p> <p>The workshop is the second part of series of workshops all limited to two hours. It uses the methods of <em>Design Thinking</em> and <em>Participatory Design</em>.</p> <p>The workshop has been recorded in video and this data set contains a textual German transcript, transcripts of the moderation cards that have been created during the workshop, invitations of the participants, additional material needed to conduct the workshop (timetable, moderation concept, etc.), and material used during the workshop by the participants (leaflets, slides, etc.).</p> <p>We hope that the material can be used to (a) comprehend the interpretation used in our qualitative research, (b) to adapt the workshop model by other volunteers of our case study Viva con Agua de St. Pauli e.V. (<a href="https://www.vivaconagua.org/">https://www.vivaconagua.org/</a>), and (c) investigate other interesting research questions.</p>
Assessing user stories: the influence of template differences and gender-related problem-solving styles - Supplemental material
<p>Here we include the supplementary material that may be used as a replication package for the quasi-experiment reported in the paper "Assessing user stories: the influence of template differences and gender-related problem-solving styles", submitted to REJ Special Issue - RE 2021.</p>
User Stories
<p>User stories dataset for neural network training</p>
Material of the paper "Deriving Domain Models from User Stories: Human vs. Machines"
<p>This folder represents the online appendix for the paper "Deriving Domain Models from User Stories: Human vs. Machines" It contains all relevant code, results, and data sets.</p>
Refining User Stories via Example Mapping: An Empirical Investigation | Online Appendix
<p>This is the online appendix for the paper "Refining User Stories via Example Mapping: An Empirical Investigation" which contains three files:</p> <ul> <li>Refinement Evaluation Tool.pdf - this file includes the RET as presented in the paper (with both the Long Questionnaire and Session Questionnaire)</li> <li>Fizor Results.xlsx - This file includes the data of the Fizor case study. It has three tabs: the Long Questionnaire results, the Session Questionnaire results and the Observation results.</li> <li>Results Pension Management Example Mapping.xlsx - This file includes the data of the pension management firm case study. It has three tabs: the Long Questionnaire results, the Session Questionnaire results and the Observation results.</li> </ul>
On the impact of different templates on creating and understanding user stories - Supplemental Material
<p>Here we include the supplementary material that may be used as a replication package for the quasi-experiment reported in the paper "On the impact of different templates on creating and understanding user stories", submitted to RE 2021 (International Requirements Engineering Conference).</p>
Extraction of Conceptual Models: User Stories vs. Use Cases
<p>This online appendix provides the materials of an experiment regarding the performance of individual subjects (24 students) in the extraction of conceptual models from a specification expressed:</p> <ul> <li>[IV1] as user stories or use cases</li> <li>[IV2] for one of three types of fictitious systems: hospital management system, urban traffic simulator, and international football association portal</li> </ul> <p>We measure the performance in terms of validity (DV1) and completeness (DV2) against models that were created by domain experts (the three authors of the paper and of this online appendix) from each of the specifications.</p> <p>The materials include</p> <ul> <li>The description of the three systems (folder System Descriptions)</li> <li>An Excel spreadsheet that includes raw data, charts, and statistical results</li> <li>The guidelines that the authors used in assessing the quality of the subjects' models against the expert models</li> <li>For each student who participated in the experiment and gave consent, <ul> <li>The specification, either via user stories or use cases, created by the student</li> <li>The student model created by the student</li> <li>The expert model created by one of the paper authors<br> Note that these files are highlighted to denote how we applied the tagging guidelines</li> </ul> </li> </ul>
ScienceDex guides
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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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DANDI Archive for NWB datasets
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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.