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22 results for “Requirements Elicitation”
Experiment package for Elicitation of Adaptive Requirements Using Creativity Triggers: A Controlled Experiment
<p>Full experimental materials, scripts, and results for "Elicitation of Adaptive Requirements Using Creativity Triggers: A Controlled Experiment"</p>
Strategies, Benefits and Challenges App Store-inspired Requirements Elicitation - Supplementary Material
<p>This is the supplementary material for the paper "Strategies, Benefits and Challenges App Store-inspired Requirements Elicitation". </p> <p>Abstract: App store-inspired elicitation is the practice of exploring competitors’ apps, to get inspiration for requirements. This activity is common among developers, but little insight is available on its practical use, advantages and possible issues. This paper aims to study strategies, benefits and challenges of app store-inspired elicitation, and compare this technique with more traditional requirements elicitation interviews. We conduct an experimental simulation with 58 analysts, and collect qualitative data. Our results show that specific guidelines and procedures are required to better conduct app store-inspired elicitation. Furthermore, current search features made available by app stores are not suitable for this practice, and more tool support is required to help analysts in the retrieval and<br> evaluation of competing products. While interviews focus on the why dimension of requirements engineering (i.e., goals), app store-inspired elicitation focuses on how (i.e., solutions), offering indications for implementation and improved usability. Our study provides a framework for researchers to address existing challenges, and suggests possible benefits to foster app store-inspired elicitation among practitioners.</p> <p>The package contains the following files:</p> <p>1.Protocol.pdf - it describes in details the steps of the protocol and the intermediate results obtained during the execution.</p> <p>2. Codebooks:<br> 2.a. Codebook Strategies: codebook of the strategies to select apps<br> 2.b Codebook Benefits: codebook of the benefits of use IBE (sheet 1) and ASE (sheet 2)<br> 2.c Codebook Challenges: codebook of the challenged of use IBE (sheet 1) and ASE (sheet 2)<br> 2.d Differences IBE-ASE: table of the identified (categorized) differences between IBE and ASE</p> <p>3. Labelled Data <br> 3.a Strategies - labelled data: the file contains the name of the selected apps, the motivation behind the selection, and the themes assigned to them (refer to 2.a for explanation of the themes).<br> 3.b Benefits IBE - labelled data: the file contains the extract of the raw data about IBE benefits and the themes assigned (refer to 2.b for explanation of the themes).<br> 3.c Challenges IBE - labelled data: the file contains the extract of the raw data about IBE challenges and the themes assigned (refer to 2.c for explanation of the themes).<br> 3.d Benefits ASE - labelled data: the file contains the extract of the raw data about ASE benefits and the themes assigned (refer to 2.b for explanation of the themes).<br> 3.e Challenges ASE - labelled data: the file contains the extract of the raw data about IBE benefits and the themes assigned (refer to 2.c for explanation of the themes).</p> <p>4. Raw data.xls: it contains the raw data used in the work (two sheets, one for strategies and one for reflections).</p> <p>5. SLR data: data related to the lightweight systematic literature review <br> 5.a Codebook Scopus.xlsx: codebook for the themes elicited from the SLR. The themes are also present in the files in the folder Codebooks.<br> 5.b SLR-scopus-results-and-selected.xlsx: results of the search string, and, in green, the selected papers. </p> <p>6. Readme.txt: summary file. </p> <p>Note that some of the row in Raw data.xls (and in the corresponding "Labelled Data" files) are substituted with N/A. This corresponds to those participants who asked to not publicly share their responses.</p>
Requirements elicitation (ReqElic) in my company
<p>Questionnaire (online survey) about requirements elicitation (ReqElic) in my company, PDF generated from <https://docs.google.com/forms/d/1RH_oMgpreDCvHexh4dKe40EVhsoBXPaXbbOYMITQDlQ/edit></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>
Supplementary Material for Use of Journey Maps and Personas in Software Requirements Elicitation
<p>This repository contains supplementary material for the "Use of Journey Maps and Personas in Software Requirements Elicitation" article.</p> <p>Context: Requirements elicitation is a fundamental step in a software development process since it is at this stage that the software begins to be designed. In some situations, the problems related to the failure of the software development project are due to an incomplete requirements elicitation, resulting in solutions that do not understand all the necessary functionalities or do not incorporate innovation. Despite the various techniques offered by Requirements Engineering, situations such as the growing application market and the need for innovation further increase the importance of understanding the user's different needs. Objective: In this paper, we investigated how the journey map and personas techniques are being used in requirements elicitation in both the literature and the industry, along with the advantages, disadvantages, and challenges of using these techniques. Method: We conducted systematic literature review to identify the personas and journey map techniques used in requirements elicitation in the literature and industry. In addition, we conducted a survey with 27 practitioners (software developers, users, and managers) to investigate their perceptions of the use of journeys map and personas techniques in the requirements elicitation phase. Results: Twenty-three primary studies were identified that address journey map and personas techniques in software requirements elicitation. In addition, most respondents stated that using these techniques facilitates understanding the requirements, providing better integration, collaboration, and leveling of knowledge among the members of the software development teams. Conclusions: Our findings allow us to conclude that most of the software developers, users, and managers that participated in the survey consider that the journey map and personas techniques are effective in helping understand the software requirements to be developed by the development teams.</p>
Supplementary Material for Creativity and Design Thinking as Facilitators in Requirements Elicitation
<p>Supplementary Material for the paper <em>Creativity and Design Thinking as Facilitators in Requirements Elicitation.</em></p> <p>The <em>survey_questions</em>.<em>pdf</em> file contains the form questions used to conduct the survey, the <em>survey_responses.csv</em> file contains the responses to this form, and the <em>table_techniques.pdf </em>file contains a supplementary table with creativity techniques and design thinking techniques.</p>
Evaluation of tracking devices and elicitation of wearability requirements for animal-centred biotelemetry in cats
<p>Thirteen cat participants wearing GPS devices were monitored through ethologically-informed observations, designed specifically to measure the behaviour of the animals with the biotelemetry tags. Here, findings from the behavioural analysis are presented.</p>
Guide for Artificial Intelligence Ethical Requirements Elicitation - RE4AI Ethical Guide - Supplementary Material
<p>This is the Supplementary Material provided for the work accepted on HICSS 55 - 2022.</p> <p>Title of work: Guide for Artificial Intelligence Ethical Requirements Elicitation - RE4AI Ethical Guide.</p> <p> </p> <p>Guide can be found here: <a href="https://josesiqueira.github.io/RE4AIEthicalGuide/index.html">https://josesiqueira.github.io/RE4AIEthicalGuide/index.html</a></p> <p>Guide source code can be found here: <a href="https://github.com/josesiqueira/RE4AIEthicalGuide">https://github.com/josesiqueira/RE4AIEthicalGuide</a></p> <p> </p> <p>This file presents 4 Tables that add significant information to the paper.</p> <p>Table 1 shows ethical principles present in ECCOLA method by Vakkuri et al. [1].</p> <p>Table 2 shows principles and their ethical issues presented in the work of Ryan and Stahl [2], as it is.</p> <p>Table 3 shows a standardisation of the principles in ECCOLA with the principles in Ryan and Stahl through a mapping.</p> <p>Table 4 presents a mapping of tools found in our previous study [3] with principles and ethical issues presented by Ryan and Stahl [2].</p> <p> </p> <p>References:</p> <p>[1] Vakkuri, K. Kemell, and P. Abrahamsson, “ECCOLA - a method for implementing ethically aligned AI systems,” CoRR, vol. abs/2004.08377, 2020.</p> <p>[2] M. Ryan and B. C. Stahl, “Artificial intelligence ethics guidelines for developers and users: clarifying their content and normative implications,” Journal of Information, Communication and Ethics in Society,2020.</p> <p>[3] J. A. Siqueira De Cerqueira, L. Dos Santos Althoff, P. Santos De Almeida, and E. Dias Canedo, “Ethical perspectives in ai: A two-folded exploratory study from literature and active development projects,” in Proceedings of the 54th Hawaii International Conference on System Sciences, p. 5240, 2021.</p>
Strategies, Benefits and Challenges App Store-inspired Requirements Elicitation - Supplementary Material
<p>This is the supplementary material for the paper "Strategies, Benefits and Challenges App Store-inspired Requirements Elicitation" (also included in this repository). </p> <p>Abstract: App store-inspired elicitation is the practice of exploring competitors’ apps, to get inspiration for requirements. This activity is common among developers, but little insight is available on its practical use, advantages, and possible issues. This paper aims to study strategies, benefits, and challenges of app store-inspired elicitation, and compare this technique with more traditional requirements elicitation interviews. We conduct an experimental simulation with 58 analysts and collect qualitative data. Our results show that specific guidelines and procedures are required to better conduct app store-inspired elicitation. Furthermore, current search features made available by app stores are not suitable for this practice, and more tool support is required to help analysts in the retrieval and<br> evaluation of competing products. While interviews focus on the why dimension of requirements engineering (i.e., goals), app store-inspired elicitation focuses on how (i.e., solutions), offering indications for implementation and improved usability. Our study provides a framework for researchers to address existing challenges and suggests possible benefits to foster app store-inspired elicitation among practitioners.</p> <p>The package contains the following files:</p> <p>1.Protocol.pdf - it describes in details the steps of the protocol and the intermediate results obtained during the execution.</p> <p>2. Codebooks:<br> 2.a. Codebook Strategies: codebook of the strategies to select apps<br> 2.b Codebook Benefits: codebook of the benefits of use IBE (sheet 1) and ASE (sheet 2)<br> 2.c Codebook Challenges: codebook of the challenged of use IBE (sheet 1) and ASE (sheet 2)<br> 2.d Differences IBE-ASE: table of the identified (categorized) differences between IBE and ASE</p> <p>3. Labelled Data <br> 3.a Strategies - labeled data: the file contains the name of the selected apps, the motivation behind the selection, and the themes assigned to them (refer to 2.a for the explanation of the themes).<br> 3.b Benefits IBE - labeled data: the file contains the extract of the raw data about IBE benefits and the themes assigned (refer to 2.b for the explanation of the themes).<br> 3.c Challenges IBE - labeled data: the file contains the extract of the raw data about IBE challenges and the themes assigned (refer to 2.c for the explanation of the themes).<br> 3.d Benefits ASE - labeled data: the file contains the extract of the raw data about ASE benefits and the themes assigned (refer to 2.b for the explanation of the themes).<br> 3.e Challenges ASE - labeled data: the file contains the extract of the raw data about IBE benefits and the themes assigned (refer to 2.c for the explanation of the themes).</p> <p>4. Raw data.xls: it contains the raw data used in the work (two sheets, one for strategies and one for reflections).</p> <p>5. SLR data: data related to the lightweight systematic literature review <br> 5.a Codebook Scopus.xlsx: codebook for the themes elicited from the SLR. The themes are also present in the files in the folder Codebooks.<br> 5.b SLR-scopus-results-and-selected.xlsx: results of the search string, and, in green, the selected papers. </p> <p>6. Readme.txt: summary file. </p> <p>Note that some of the rows in Raw data.xls (and in the corresponding "Labelled Data" files) are substituted with N/A. This corresponds to those participants who asked to not publicly share their responses.</p>
Experimental material of the article "An Empirical Experiment of a Usability Requirements Elicitation Method based on Interviews"
<p>Questionnaires, problems description and solution, and raw data of the paper "An Empirical Experiment of a Usability Requirements Elicitation Method based on Interviews"</p>
Replication Package - How Do Requirements Evolve During Elicitation? An Empirical Study Combining Interviews and App Store Analysis
<p>This is the replication package for the paper titled "How Do Requirements Evolve During</p> <p>Elicitation? An Empirical Study Combining Interviews and App Store Analysis", by Alessio Ferrari, Paola Spoletini and Sourav Debnath.</p> <p> </p> <p>The package contains the following folders and files. </p> <p> </p> <p>**<strong>/Experiment Material</strong>**</p> <p>This folder contains the material used for the experiment, and provided to the participants.</p> <p>In particular, it includes the following files:</p> <p> </p> <p>- Happy CampingTM_briefdescription.pdf/docx: brief description of the product for which requirements need to be elicited</p> <p>- Hw_description.pdf/docx: desciption of the tasks to be performed by the participants</p> <p>- Modeling_Intro_Slides.pdf: introductory slides to modelling for requirements engineering</p> <p>- Self-assessment Questionnaire.pdf: first questionnaire to self-assess the mistakes, from the SaPeer method (https://doi.org/10.1007/s00766-020-00334-0) </p> <p>- Self-assessment Questionnaire (Second Interview).pdf: second questionnare to self-assess the mistakes, from the Sapeer method</p> <p> </p> <p>**<strong>/R-analysis</strong>**</p> <p> </p> <p>This is a folder containing all the R implementations of the the statistical tests included in the paper, together with the source .csv file used to produce the results. Each R file has the same title as the associated .csv file. The titles of the files reflect the RQs as they appear in the paper. The association between R files and Tables in the paper is as follows:</p> <p> </p> <p>- RQ1-1-analyse-story-rates.R: Tabe 1, user story rates </p> <p>- RQ1-1-analyse-role-rates.R: Table 1, role rates</p> <p>- RQ1-2-analyse-story-category-phase-1.R: Table 3, user story category rates in phase 1 compared to original rates</p> <p>- RQ1-2-analyse-role-category-phase-1.R: Table 5, role category rates in phase 1 compared to original rates</p> <p>- RQ2.1-analysis-app-store-rates-phase-2.R: Table 8, user story and role rates in phase 2</p> <p>- RQ2.2-analysis-percent-three-CAT-groups-ph1-ph2.R: Table 9, comparison of the categories of user stories in phase 1 and 2</p> <p>- RQ2.2-analysis-percent-two-CAT-roles-ph1-ph2.R: Table 10, comparison of the categories of roles in phase 1 and 2. </p> <p> </p> <p>The .csv files used for statistical tests are also used to produce boxplots. The association betwee boxplot figures and files is as follows. </p> <p> </p> <p>- RQ1-1-story-rates.csv: Figure 4 </p> <p>- RQ1-1-role-rates.csv: Figure 5</p> <p>- RQ1-2-categories-phase-1.csv: Figure 8</p> <p>- RQ1-2-role-category-phase-1.csv: Figure 9</p> <p>- RQ2-1-user-story-and-roles-phase-2.csv: Figure 13</p> <p>- RQ2.2-percent-three-CAT-groups-ph1-ph2.csv: Figure 14</p> <p>- RQ2.2-percent-two-CAT-roles-ph1-ph2.csv: Figure 17</p> <p>- IMG-only-RQ2.2-us-category-comparison-ph1-ph2.csv: Figure 15</p> <p>- IMG-only-RQ2.2-frequent-roles.csv: Figure 18</p> <p> </p> <p>NOTE: The last two .csv files do not have an associated statistical tests, but are used solely to produce boxplots.</p> <p> </p> <p>**<strong>/Data-Analysis</strong>**</p> <p> </p> <p>This folder contains all the data used to answer the research questions. </p> <p> </p> <p>**<strong>RQ1.xlsx</strong>**: includes all the data associated to RQ1 subquestions, two tabs for each subquestion (one for user stories and one for roles). The names of the tabs are self-explanatory of their content.</p> <p> </p> <p>**<strong>RQ2.1.xlsx</strong>**: includes all the data for the RQ1.1 subquestion. Specifically, it includes the following tabs:</p> <p> </p> <p>* Data Source-US-category: for each category of user story, and for each analyst, there are two lines. </p> <p>The first one reports the number of user stories in that category for phase 1, and the second one reports the</p> <p>number of user stories in that category for phase 2, considering the specific analyst. </p> <p> </p> <p>* Data Source-role: for each category of role, and for each analyst, there are two lines. </p> <p>The first one reports the number of user stories in that role for phase 1, and the second one reports the</p> <p>number of user stories in that role for phase 2, considering the specific analyst. </p> <p> </p> <p>* RQ2.1 rates: reports the final rates for RQ2.1. </p> <p>NOTE: The other tabs are used to support the computation of the final rates.</p> <p> </p> <p>**<strong>RQ2.2.xlsx</strong>**: includes all the data for the RQ2.2 subquestion. Specifically, it includes the following tabs:</p> <p> </p> <p>* Data Source-US-category: same as RQ2.1.xlsx</p> <p> </p> <p>* Data Source-role: same as RQ2.1.xlsx</p> <p> </p> <p>* RQ2.2-category-group: comparison between groups of categories in the different phases, used to produce Figure 14</p> <p> </p> <p>* RQ2.2-role-group: comparison between role groups in the different phases, used to produce Figure 17</p> <p> </p> <p>* RQ2.2-specific-roles-diff: difference between specific roles, used to produce Figure 18</p> <p> </p> <p>**<strong>NOTE:</strong>** the other tabs are used to support the computation of the values reported in the tabs above. </p> <p> </p> <p>**<strong>RQ2.2-single-US-category.xlsx</strong>**: includes the data for the RQ2.2 subquestion associated to single categories of user stories.</p> <p>A separate tab is used given the complexity of the computations. </p> <p> </p> <p>* Data Source-US-category: same as RQ2.1.xlsx</p> <p> </p> <p>* Totals: total number of user stories for each analyst in phase 1 and phase 2</p> <p> </p> <p>* Results-Rate-Comparison: difference between rates of user stories in phase 1 and phase 2, used to produce the file</p> <p>"img/IMG-only-RQ2.2-us-category-comparison-ph1-ph2.csv", which is in turn used to produce Figure 15</p> <p> </p> <p>* Results-Analysts: number of analysts using each novel category produced in phase 2, used to produce Figure 16.</p> <p>NOTE: the other tabs are used to support the computation of the values reported in the tabs above. </p> <p> </p> <p>**<strong>RQ2.3.xlsx</strong>**: includes the data for the RQ2.3 subquestion. Specifically, it includes the following tabs:</p> <p> </p> <p>* Data Source-US-category: same as RQ2.1.xlsx</p> <p> </p> <p>* Data Source-role: same as RQ2.1.xlsx</p> <p> </p> <p>* RQ2.3-categories: novel categories produced in phase 2, used to produce Figure 19</p> <p> </p> <p>* RQ2-3-most-frequent-categories: most frequent novel categories</p> <p> </p> <p>**<strong>/Raw-Data-Phase-I</strong>**</p> <p>The folder contains one Excel file for each analyst, s1.xlsx...s30.xlsx, plus the file of the original user stories with annotations (original-us.xlsx). Each file contains two tabs:</p> <p> </p> <p>- Evaluation: includes the annotation of the user stories as existing user story in the original categories (annotated with "E"), novel user story in a certain category (refinement, annotated with "N"), and novel user story in novel category (Name of the category in column "New Feature"). **<strong>NOTE 1:</strong>** It should be noticed that in the paper the case "refinement" is said to be annotated with "R" (instead of "N", as in the files) to make the paper clearer and easy to read. </p> <p> </p> <p>- Roles: roles used in the user stories, and count of the user stories belonging to a certain role.</p> <p> </p> <p>**<strong>/Raw-Data-Phaes-II</strong>**</p> <p>The folder contains one Excel file for each analyst, s1.xlsx...s30.xlsx. Each file contains two tabs:</p> <p> </p> <p>- Analysis: includes the annotation of the user stories as belonging to existing original </p> <p>category (X), or to categories introduced after interviews, or to categories introduced </p> <p>after app store inspired elicitation (name of category in "Cat. Created in PH1"), or to </p> <p>entirely novel categories (name of category in "New Category").</p> <p> </p> <p>- Roles: roles used in the user stories, and count of the user stories belonging to a certain role.</p> <p> </p> <p>**<strong>/Figures</strong>**</p> <p> </p> <p>This folder includes the figures reported in the paper. The boxplots are generated from the </p> <p>data using the tool http://shiny.chemgrid.org/boxplotr/. The histograms and other plots are </p> <p>produced with Excel, and are also reported in the excel files listed above. </p>
Online Appendix of "Crowd-based Requirements Elicitation via Pull Feedback: Method and Case Studies"
<p>The online appendix contains the data sets used in the paper Crowd-based Requirements Elicitation via Pull Feedback: Method and Case Studies, by Jelle Wouters, Abel Menkveld, Sjaak Brinkkemper and Fabiano Dalpiaz. It consists of three data sets, a process-deliverable diagram, a file that contains charts, and two Jupyter (Python) notebook scripts. In this readme, we briefly explain how files should be read and interpreted.</p> <p><strong>Dataset-Tournify.xlsx</strong></p> <p>This file contains all data of the Tournify case. The following tabs are present:</p> <ul> <li> <p>Raw data: contains the raw data collected from the Tournify CrowdRE platform.</p> </li> <li> <p>Automatically translated data: The readability and vagueness measures were calculated using English text. Most of the ideas collected were in Dutch, so the raw data was translated into English using a Google Translator API.</p> </li> <li> <p>Readability scores: Consists of the Flesch and ARI readability scores, calculated using the Python scripts (see below). </p> </li> <li> <p>Vague hits: Consists of all the vague words found in the Tournify ideas. We identified those using a Python script. Using numbers we identified whether the hit was a True Positive (TP) or was a false positive, and in which category the false positive lied. </p> </li> <li> <p>Tagging-50-FD & Tagging-50-JW: These tabs contain the tagging of the ideas on qualities of the QUS-framework and the ISO/IEC 25010. Two researchers did this independently from another.</p> </li> <li> <p>Compare-50: This sheet consists of all ideas for which a disparity exists between the two fields. When a disagreement exists, the field is colored green. By text in the field, we indicated what the final decision was. </p> </li> <li> <p>Result-50: This tab combines the results and shows the final decision after deliberation between the two authors.</p> </li> <li> <p>Tagging-195-FB, Tagging-195-JW, Compare-195, Result-195: Same as above, but for the other 195 ideas in the case (we split this data set in two to try out the modus operandi first).</p> </li> <li> <p>Result-total: Combines the Result-50 and the Result-195 sheet. Colored cells indicate that we marked the idea to be considered to present verbatim in the paper. </p> </li> <li> <p>Kappa-scores: Calculates the Kappa-scores that are presented in the paper.</p> </li> </ul> <p><strong>Dataset-SSys.xlsx and Dataset-VSys.xlsx</strong></p> <p><strong>This file contains all data for respectively the S-Sys and V-Sys cases. The following tabs are present:</strong></p> <ul> <li> <p>Raw data: consists of the raw data collected in the CrowdRE platform of the case. As can be seen, some data is ‘not published’ (but was analyzed in the study and read by both researchers and therefore just redacted in the online appendix), and some data is ‘classified’ (and therefore not analyzed by both researchers as one researcher was not allowed to review the data). The classified ideas should be considered as non-existent in the rest of the data set. </p> </li> <li> <p>Automatically translated data, readability scores: These files are compiled in the same way as in the Tournify case.</p> </li> <li> <p>Vagueness: This file is compiled in the same way as in the Tournify case, although we do provide a small explanation that describes a part of the idea to show why a true or false positive was indicated.</p> </li> <li> <p>Tagging: The tagging of the QUS-framework and ISO/IEC 25010 was done here. As we did this in person, the small discussion held when discrepancies occurred is not presented in the file. The colored cells indicate ideas we considered for verbatim publication in the paper. The colors indicate why we want to publish a certain idea (for example, because it has all QUS-violations).</p> </li> <li> <p>Kappa-scores: This shows the kappa scores and the discrepancies between the individual tagging. This is indicated in colors. </p> </li> </ul> <p><strong>Graphs_readability.aspx</strong></p> <p>This file was used to construct the graphs as presented in figures six and seven in the paper. For this, the readability scores of the three data sets were combined in one tab (one for Flesch and one for ARI) and used in a boxplot.</p> <p><strong>CREUS-pdd.drawio</strong></p> <p>This is the source file for the PDD presented in the paper.</p> <p><strong>Python scripts</strong></p> <p>Two Jupyter notebooks that "S-Sys V-Sys.ipynb" and "Tournify.ipynb" that we used to calculate the readability scores and to identify the vague words. As the data sets were structured a bit differently, the scripts are a bit different between the three cases. In the Tournify case, the script and the output are both present in the Jupyter notebook. For the S-Sys and V-Sys cases, we do include the script but omit the output due to confidentiality.</p> <p><strong>Note:</strong></p> <p>The data sets are included in our paper to allow readers to explore the data themselves. Please contact the corresponding author if you wish to use the data set for other reasons to obtain an explicit permission, since these user stories should be analyzed with proper domain knowledge in order to draw meaningful conclusions.</p>
Replication Package for the Paper: Identifying Key Factors for Using Ethnography in Software Requirements Elicitation - A Systematic Literature Review
<p>This is the replication package for the paper: "Identifying Key Factors for Using Ethnography in Software Requirements Elicitation - A Systematic Literature Review"</p> <p>It contains:</p> <ul> <li>Review protocol (in spanish)</li> <li>Extracted data for each research question</li> <li>List of primary studies</li> <li>Appendix</li> </ul> <p> </p>
Online appendix to Summarization of Elicitation Conversations to Locate Requirements-Relevant Information
<p>This is the online appendix of the paper "Summarization of Elicitation Conversations to Locate Requirements-Relevant Information", published at REFSQ'23.</p> <p>The file includes the following:</p> <ul> <li>A copy of the source code (folder REConSum-main/code) - see the README file in REConSum-main for instructions on how to run this. This is an archived copy of the GitHub repository: https://github.com/RELabUU/REConSum</li> <li>The empirical results obtained by executing REConSum (subfolder REConSum-main/results) </li> <li>An example of a requirements conversation (subfolder REConSum-main/data)</li> <li>The tagging guide that was given to the people who participated in the construction of the golden standard (tagging-guide.pdf)</li> <li>The tagging results, which can be used to verify the inter-rater agreement (tagging-results.xlsx)</li> </ul>
Supplementary Material for On the Experiences of Practitioners with Requirements Elicitation Techniques
<p>Supplementary Material for the paper entitled "<em>On the Experiences of Practitioners with Requirements Elicitation Techniques</em>".</p> <p><strong>Abstract</strong></p> <p>Requirements elicitation is a crucial process in software engineering, which involves identifying and understanding the needs of stakeholders to define system requirements. Several techniques are used for requirements elicitation, each with unique advantages, disadvantages, and challenges. This paper presents the findings of a survey conducted among 33 practitioners in the software development community to investigate their experiences with requirements elicitation techniques. The results revealed that practitioners find the elicitation process highly challenging due to difficulties managing the relationship between the development team and the client, understanding complex business processes, and the lack of knowledge among stakeholders. The survey also assessed the participants' familiarity with various elicitation techniques. The most well-known techniques were brainstorming, data analysis, use cases, interviews, user stories, and prototyping. In contrast, techniques such as ethnography, Quality Function Deployment (QFD), Joint Application Development (JAD), blueprint, and laddering were less recognized. When providing the pros and cons of some techniques, participants considered techniques' clarity, speed of use, resource cost, and stakeholder involvement. This research contributes to the field by highlighting challenges, providing insights into practitioner experiences, and guiding informed decision-making in requirements elicitation.</p>
Supplementary material for the study titled "Investigating ChatGPT's Potential in the Requirements Elicitation Process"
<p>This supplementary material folder consists of ChatGPT-generated responses to 6 questions asked to elicit requirements for the development of Trustworthy AI. This also has a glossary of Trustworthy AI qualities and requirement quality attributes used to evaluate the responses consisting of Trustworthy AI requirements mentioned above. </p>
Eliciting Public Discourse of SE Tool Providers in a Study on Requirements Process Debt – A Different Shade of Grey - Auxiliary Material
Open the record for dataset details and reuse information.
B cells require DOCK8 to elicit and integrate T cell help when antigen is limiting
GEO Series GSE269130. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing; Other.
Engineered mammalian RNAi can elicit antiviral protection that negates the requirement for the interferon response
GEO Series GSE73698. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Plant recognition by Trichoderma harzianum elicits upregulation of a novel secondary metabolite cluster required for colonization
GEO Series GSE229209. Trichoderma harzianum. 6 samples. Type: Expression profiling by high throughput sequencing.
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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.