Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
9
datasets available to search
ShareScore release 0.9.0
Dataset results
9 results for “App Store”
A Replication Package of Learning Features that Predict Developer Responses for iOS App Store Reviews
<p>This replication package contains the dataset and script used in our paper "<em>Learning Features that Predict Developer Responses for iOS App Store Reviews.</em>" The paper has been accepted at the ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), 2020. For further modification and versioning of the dataset (as well as the preprint) please go to <a href="https://github.com/Kamonphop/ESEM20-Replication">https://github.com/Kamonphop/ESEM20-Replication</a></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>
A Large-Scale Empirical Study of Android Sports Apps in the Google Play Store
<p>This repository contains the dataset for our study "A Large-Scale Empirical Study of Android Sports Apps in the Google Play Store" and this will help to replicate our study, also the <a href="https://github.com/mooselab/Sports-Apps-Analysis">replication package</a> to direct you to help replicate it for your dataset too. </p> <p>Note: The dataset given are protected with password, and the password is available in our published paper</p>
Analyzing App Store Comments and Quality Attributes for Defining an Inspection Checklist for Mobile Educational Games
<p>To evaluate educational games, several techniques have been proposed considering different quality attributes. However, there are still several educational games for the mobile context that have low scores in the app stores. These stores allow users to make comments to evaluate the applications, as this data can be useful for the development team that aims to meet users' expectations. The analysis of comments made by users can help identify which attributes impact the use of mobile educational games. In this paper, an inspection checklist is proposed to evaluate mobile educational games. To complement the attributes identified in the analysis of comments, attributes from existing techniques for evaluating mobile educational games were also considered. The final evaluation form contains a total of 82 attributes distributed in evaluation categories, such as: user interface, mobility, pedagogy, gameplay, among others. To evaluate the proposed technique, an evaluation was carried out with the checklist in two mobile educational games available in the Google Play Store, different from those used to define the technique. The initial results indicate that the proposed checklist allows the identification of problems pointed out by users in the comments left in the app store.</p> <p> </p> <p><a href="https://zenodo.org/api/files/8ca1af4d-6f7c-440f-a272-9366de2ad3ef/SBES%202020%20-%20Ideias%20Inovadoras%20e%20Resultados%20Emergentes%20-%20Analisando%20atributos%20de%20qualidade%20e%20coment%C3%A1rios%20de%20lojas%20de%20aplicativos%20para%20a%20defini%C3%A7%C3%A3o%20de%20uma%20t%C3%A9cnica%20de%20inspe%C3%A7%C3%A3o%20de%20jogos%20educacionais%20m%C3%B3veis.mp4">SBES 2020 - Ideias Inovadoras e Resultados Emergentes</a></p>
Dataset used for "A Recommender System of Buggy App Checkers for App Store Moderators"
<p>This is the dataset used for paper: "A Recommender System of Buggy App Checkers for App Store Moderators", published on the <em>International Conference on Mobile Software Engineering and Systems (MOBILESoft)</em> in 2015.<br> <br> <strong>Dataset Collection</strong><br> We built a dataset that consists of a random sample of <em><strong>Android app metadata</strong></em> and <em><strong>user reviews</strong></em> available on the <em>Google Play Store</em> on January and March 2014.<br> Since the Google Play Store is continuously evolving (adding, removing and/or updating apps), we updated the dataset twice.<br> The dataset D1 contains available apps in the Google Play Store in January 2014.<br> Then, we created a new snapshot (D2) of the Google Play Store in March 2014.<br> <br> The apps belong to the 27 different categories defined by Google (at the time of writing the paper), and the 4 predefined subcategories (free, paid, new_free, and new_paid). For each category-subcategory pair (e.g. tools-free, tools-paid, sports-new_free, etc.), we collected a maximum of 500 samples, resulting in a median number of 1.978 apps per category.</p> <p>For each app, we retrieved the following metadata: <em>name, package, creator, version code, version name, number of downloads, size, upload date, star rating, star counting</em>, and the set of <em>permission requests</em>.<br> <br> In addition, for each app, we collected up to a maximum of the latest 500 reviews posted by users in the Google Play Store. For each review, we retrieved its metadata:<em> title, description, device,</em> and <em>version</em> of the app. None of these fields were mandatory, thus<br> several reviews lack some of these details.<br> From all the reviews attached to an app, we only considered the reviews associated with the latest version of the app —i.e., we discarded unversioned and old-versioned reviews. Thus, resulting in a corpus of 1,402,717 reviews (2014 Jan.).</p> <p> </p> <p><strong>Dataset Stats</strong><br> Some stats about the datasets:</p> <p>- <strong>D1</strong> (<em>Jan. 2014</em>) contains 38,781 apps requesting 7,826 different permissions, and 1,402,717 user reviews.</p> <p>- <strong>D2</strong> (<em>Mar. 2014</em>) contains 46,644 apps and 9,319 different permission requests, and 1,361,319 user reviews.</p> <p>Additional stats about the datasets are available <a href="https://sites.google.com/site/androidbuggyappcheckers">here</a>.<br> <br> <br> <strong>Dataset Description</strong><br> To store the dataset, we created a graph database with <a href="https://neo4j.com/">Neo4j</a>. This dataset therefore consists of a graph describing the apps as nodes and edges. We chose a graph database because the graph visualization helps to identify connections among data (e.g.,<br> clusters of apps sharing similar sets of permission requests).<br> <br> In particular, our dataset graph contains six <em>types of nodes</em>:<br> - <em>APP</em> nodes containing metadata of each app,<br> - <em>PERMISSION</em> nodes describing permission types,<br> - <em>CATEGORY</em> nodes describing app categories,<br> - <em>SUBCATEGORY</em> nodes describing app subcategories,<br> - <em>USER_REVIEW</em> nodes storing user reviews.<br> - <em>TOPIC</em> topics mined from user reviews (using LDA).<br> <br> Furthermore, there are five <em>types of relationships</em> between APP nodes and each of the remaining nodes:</p> <p>- <em>USES_PERMISSION</em> relationships between APP and PERMISSION nodes<br> - <em>HAS_REVIEW</em> between APP and USER_REVIEW nodes<br> - <em>HAS_TOPIC</em> between USER_REVIEW and TOPIC nodes<br> - <em>BELONGS_TO_CATEGORY</em> between APP and CATEGORY nodes<br> - <em>BELONGS_TO_SUBCATEGORY</em> between APP and SUBCATEGORY nodes</p> <p><br> <strong>Dataset Files Info</strong></p> <ul> <li><strong>Neo4j 2.0 Databases</strong> <ul> <li><em><strong>googlePlayDB1-Jan2014_neo4j_2_0.rar</strong></em></li> <li><em><strong>googlePlayDB2-Mar2014_neo4j_2_0.rar</strong></em><br> We provide two Neo4j databases containing the 2 snapshots of the Google Play Store (January and March 2014). These are the original databases created for the paper. The databases were created with <strong>Neo4j 2.0. </strong>In particular with the tool version <em>'Neo4j 2.0.0-M06 Community Edition' (latest version available at the time of implementing the paper in 2014).</em><br> </li> </ul> </li> <li><strong>Neo4j 3.5 Databases</strong> <ul> <li><strong>googlePlayDB1-Jan2014_neo4j_3_5_28.rar</strong></li> <li><strong>googlePlayDB2-Mar2014_neo4j_3_5_28.rar</strong><br> Currently, the version Neo4j 2.0 is deprecated and it is not available for download in the official <em>Neo4j Download Center</em>. We have migrated the original databases (Neo4j 2.0) to Neo4j 3.5.28.<br> The databases can be opened with the tool version: <em>'Neo4j Community Edition 3.5.28'.<br> The tool can be downloaded from the official </em><a href="https://neo4j.com/download-center/#community">Neo4j Donwload</a> page.<br> <br> In order to open the databases with more recent versions of Neo4j, the databases must be first migrated to the corresponding version. Instructions about the migration process can be found in the <a href="https://neo4j.com/docs/upgrade-migration-guide/current/understanding-upgrades-migration/">Neo4j Migration Guide</a>.<br> <br> First time the Neo4j database is connected, it could request credentials. The username and pasword are: neo4j/neo4j <br> <br> </li> </ul> </li> </ul> <p> </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>
App Store data
<p>Data sets from 2 apps stores Google Play and Apple’s app stores. These data sets contain data of 30 apps belonging to 6 different categories. The data was collected from Dec 2018 to Dec 2019.</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>
App Store Reviews
<p><strong>App Store Reviews</strong></p> <ul> <li>Apple App Store</li> <li>Google Play Store</li> </ul>
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.