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677 results for “Replication package”
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>
Replication Package for the Paper: "Potential Technical Debt and Its Resolution in Code Reviews: An Exploratory Study of the OpenStack and Qt Communities"
<p>This is the replication package for the paper: "Potential Technical Debt and Its Resolution in Code Reviews: An Exploratory Study of the OpenStack and Qt Communities", including the dataset and its description (README.md):</p> <ul> <li><strong>PTD-related Comments in Code Review and Data Extraction Results.xlsx</strong> is the dataset of our paper, which contains 2,030 review comments collected from the Nova project and Neutron project of OpenStack community. Among all the review comments, 163 review comments indicate PTD. For the rows of review comments that are related to PTD, we filled them with blue color as an indicator.</li> <li> <p><strong>README.md</strong></p> </li> </ul>
Replication package for: Estimating the Costs of Standardization: Evidence from the Movie Industry
<p>Caoui, El Hadi (2022). "Estimating the Costs of Standardization: Evidence from the Movie Industry". Manuscript<br> The files create figures and tables reported in the manuscript and online appendix.</p>
Replication package for: Hours Constraints, Occupational Choice, and Gender: Evidence from Medical Residents
<p>This is the replication package for:</p> <p>Wasserman, Melanie (forthcoming) "Hours Constraints, Occupational Choice, and Gender: Evidence from Medical Residents" Review of Economic Studies</p> <p>It contains:</p> <ul> <li>README file</li> <li>do files to reproduce the main tables and figures</li> <li>data files for all reproducible analyses</li> <li>all reproducible results</li> </ul>
Replication Package for "Automating Code Review: Using Deep Learning to Assess the Quality of Code Contributions and Recommend Changes"
<p>Replication Package for "Automating Code Review: Using Deep Learning to Assess the Quality of Code Contributions and Recommend Changes"</p>
Replication package for: Monopsony power and wages: Evidence from the introduction of serfdom in Denmark
<p>This is the replication package for Gary et al. (forthcoming). It contains all data sets and STATA code to replicate all results of the paper. For a full description please see the "readme" file contained in the zip file under 3. replication package,</p>
Replication Package - IoT Roadmap Feasibility Study
<p>Replication package for a Feasibility Study.</p> <p>GOAL: This work aims to report a study with software engineers to characterize an IoT Roadmap's viability, considering the artifacts generated in the context of IoT software systems design already concluded.</p>
Replication Package - IoT Roadmap Observation Study
<p>Replication package for a Observation Study.</p> <p>GOAL: This work aims to analyze the use of the IoT Roadmap with the purpose of understanding in relation to its applicability from the point of view of junior software engineers in the context of the IoT project for COVID-19 developed at the Federal University of Rio de Janeiro.</p>
Automated inference of sequences of operations in RESTful APIs with deep reinforcement learning - replication package
<p>This directory contains the replication package of the article entitled "Automated inference of sequences of operations in RESTful APIs with deep reinforcement learning". Please note that this is NOT a real publication or scientific report, but a project created for the purpose of learning the basic concepts of research by emulating the process of creating an article.</p>
Replication package for identifying four types of SATD from four different sources
<p>This dataset includes pre-trained word embeddings and the weighted file for identifying four types (i.e., code/design, requirement, documentation, and test debt) of SATD (self-admitted technical debt) from four different sources (i.e., code comments, issue tracking systems, commit messages, and pull requests).</p>
Replication package for: Moral Universalism and the Structure of Ideology
<p>This package contains replication material (survey procedures, code, data) for the exhibits in Enke, Rodriguez-Padilla, and Zimmermann (forthcoming), "Moral Universalism and the Structure of Ideology." Review of Economic Studies.</p>
Replication package for: Culture and the Historical Fertility Transition
<p>Replication package for: Beach, Brian and Hanlon, W. Walker (Forthcoming). "Culture and the Historical Fertility Transition," Review of Economic Studies. Package contains data, processing and analysis code, and results tables.</p>
Replication package for A Risk-based Theory of Exchange Rate Stabilization
<p>The package contains the code necessary to reproduce Table 1, Figure 1 in the main text, and Table 2 in the Online Appendix in Hassan, Mertens and Zhang (2022) "A Risk-based Theory of Exchange Rate Stabilization." Review of Economic Studies. The package also includes a Mathematica notebook that derives the equations and propositions in the main text.</p>
Replication package for: "Subsidizing Labor Hoarding in Recessions: The Employment & Welfare Effects of Short Time Work"
<p>This package contains all the necessary code to replicate figures, tables and in-text statistics in Giupponi and Landais (forthcoming), "Subsidizing Labor Hoarding in Recessions: The Employment & Welfare Effects of Short Time Work", Review of Economic Studies. Detailed instructions are also provided on how to access the raw data.</p>
Replication package for "A Rise for Grandma: Pensions and Household Expenditure"
<p>Parraga Rodriguez, S. (2022). "A Rise for Grandma: Pensions and Household Expenditure", The Economic Journal.</p>
Replication package for "Investor Sentiment, Sovereign Debt Mispricing, and Economic Outcomes"
<p>Replication package for "Investor Sentiment, Sovereign Debt Mispricing, and Economic Outcomes" by Ramzy Al-Amine and Tim Willems</p>
Replication package for: "Trading Favours through the Revolving Door: Evidence from China's Primary Land Market"
<p>This package provides the datasets and programme files used to replicate the empirical results in the paper “Trading Favours through the Revolving Door: Evidence from China’s Primary Land Market” by Ting Chen, Li Han, James Kung, and Jiaxin Xie.</p>
Replication Package for "Timing Side Channel Mitigation via Automated Program Repair"
<p><strong>Pendulum: Timing Side Channel Mitigation via Automated Program Repair</strong></p> <p>This is the replication artifact for the paper "Timing Side Channel Mitigation via Automated Program Repair". It includes:</p> <ul> <li>a <strong>Docker</strong> file for easy setup and execution of Pendulum,</li> <li>the <strong>setup</strong> instructions,</li> <li>the instructions for a <strong>quick trial</strong> of the setup,</li> <li>the <strong>workflow</strong> description incl. how to <strong>apply</strong> Pendulum to other programs, and</li> <li>detailed instructions about how to <strong>replicate our experiments</strong>.</li> </ul> <p>Please read our README for more information and explanations.</p>
Replication Package for "Long-term and Intergenerational Effects of Education: Evidence from School Construction in Indonesia"
<p>Replication package for:</p> <p>Akresh, Richard, Daniel Halim, and Marieke Kleemans. 2023. "Long-term and Intergenerational Effects of Education: Evidence from School Construction in Indonesia" <em>Economic Journal</em>, forthcoming.</p>
Replication package: Evidence Profiles for Validity Threats in Program Comprehension Experiments
<p>Replication package for:</p> <p><em>M. Muñoz Barón, M. Wyrich, D. Graziotin, S. Wagner. ‘Evidence Profiles for Validity Threats in Program Comprehension Experiments’. In: 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE). IEEE</em></p>
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
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.