100+ datasets found
  1. RICO dataset

    • kaggle.com
    Updated Dec 2, 2021
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    Onur Gunes (2021). RICO dataset [Dataset]. https://www.kaggle.com/datasets/onurgunes1993/rico-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 2, 2021
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Onur Gunes
    Description

    Context

    Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. The Rico dataset contains design data from more than 9.3k Android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 66k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports query-by-example search over UIs.

    Content

    Rico was built by mining Android apps at runtime via human-powered and programmatic exploration. Like its predecessor ERICA, Rico’s app mining infrastructure requires no access to — or modification of — an app’s source code. Apps are downloaded from the Google Play Store and served to crowd workers through a web interface. When crowd workers use an app, the system records a user interaction trace that captures the UIs visited and the interactions performed on them. Then, an automated agent replays the trace to warm up a new copy of the app and continues the exploration programmatically, leveraging a content-agnostic similarity heuristic to efficiently discover new UI states. By combining crowdsourcing and automation, Rico can achieve higher coverage over an app’s UI states than either crawling strategy alone. In total, 13 workers recruited on UpWork spent 2,450 hours using apps on the platform over five months, producing 10,811 user interaction traces. After collecting a user trace for an app, we ran the automated crawler on the app for one hour.

    Acknowledgements

    UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN https://interactionmining.org/rico

    Inspiration

    The Rico dataset is large enough to support deep learning applications. We trained an autoencoder to learn an embedding for UI layouts, and used it to annotate each UI with a 64-dimensional vector representation encoding visual layout. This vector representation can be used to compute structurally — and often semantically — similar UIs, supporting example-based search over the dataset. To create training inputs for the autoencoder that embed layout information, we constructed a new image for each UI capturing the bounding box regions of all leaf elements in its view hierarchy, differentiating between text and non-text elements. Rico’s view hierarchies obviate the need for noisy image processing or OCR techniques to create these inputs.

  2. Unlocking User Sentiment: The App Store Reviews Dataset

    • crawlfeeds.com
    json, zip
    Updated Jun 20, 2025
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    Crawl Feeds (2025). Unlocking User Sentiment: The App Store Reviews Dataset [Dataset]. https://crawlfeeds.com/datasets/app-store-reviews-dataset
    Explore at:
    json, zipAvailable download formats
    Dataset updated
    Jun 20, 2025
    Dataset authored and provided by
    Crawl Feeds
    License

    https://crawlfeeds.com/privacy_policyhttps://crawlfeeds.com/privacy_policy

    Description

    This dataset offers a focused and invaluable window into user perceptions and experiences with applications listed on the Apple App Store. It is a vital resource for app developers, product managers, market analysts, and anyone seeking to understand the direct voice of the customer in the dynamic mobile app ecosystem.

    Dataset Specifications:

    • Investment: $45.0
    • Status: Published and immediately available.
    • Category: Ratings and Reviews Data
    • Format: Compressed ZIP archive containing JSON files, ensuring easy integration into your analytical tools and platforms.
    • Volume: Comprises 10,000 unique app reviews, providing a robust sample for qualitative and quantitative analysis of user feedback.
    • Timeliness: Last crawled: (This field is blank in your provided info, which means its recency is currently unknown. If this were a real product, specifying this would be critical for its value proposition.)

    Richness of Detail (11 Comprehensive Fields):

    Each record in this dataset provides a detailed breakdown of a single App Store review, enabling multi-dimensional analysis:

    1. Review Content:

      • review: The full text of the user's written feedback, crucial for Natural Language Processing (NLP) to extract themes, sentiment, and common keywords.
      • title: The title given to the review by the user, often summarizing their main point.
      • isEdited: A boolean flag indicating whether the review has been edited by the user since its initial submission. This can be important for tracking evolving sentiment or understanding user behavior.
    2. Reviewer & Rating Information:

      • username: The public username of the reviewer, allowing for analysis of engagement patterns from specific users (though not personally identifiable).
      • rating: The star rating (typically 1-5) given by the user, providing a quantifiable measure of satisfaction.
    3. App & Origin Context:

      • app_name: The name of the application being reviewed.
      • app_id: A unique identifier for the application within the App Store, enabling direct linking to app details or other datasets.
      • country: The country of the App Store storefront where the review was left, allowing for geographic segmentation of feedback.
    4. Metadata & Timestamps:

      • _id: A unique identifier for the specific review record in the dataset.
      • crawled_at: The timestamp indicating when this particular review record was collected by the data provider (Crawl Feeds).
      • date: The original date the review was posted by the user on the App Store.

    Expanded Use Cases & Analytical Applications:

    This dataset is a goldmine for understanding what users truly think and feel about mobile applications. Here's how it can be leveraged:

    • Product Development & Improvement:

      • Bug Detection & Prioritization: Analyze negative review text to identify recurring technical issues, crashes, or bugs, allowing developers to prioritize fixes based on user impact.
      • Feature Requests & Roadmap Prioritization: Extract feature suggestions from positive and neutral review text to inform future product roadmap decisions and develop features users actively desire.
      • User Experience (UX) Enhancement: Understand pain points related to app design, navigation, and overall usability by analyzing common complaints in the review field.
      • Version Impact Analysis: If integrated with app version data, track changes in rating and sentiment after new app updates to assess the effectiveness of bug fixes or new features.
    • Market Research & Competitive Intelligence:

      • Competitor Benchmarking: Analyze reviews of competitor apps (if included or combined with similar datasets) to identify their strengths, weaknesses, and user expectations within a specific app category.
      • Market Gap Identification: Discover unmet user needs or features that users desire but are not adequately provided by existing apps.
      • Niche Opportunities: Identify specific use cases or user segments that are underserved based on recurring feedback.
    • Marketing & App Store Optimization (ASO):

      • Sentiment Analysis: Perform sentiment analysis on the review and title fields to gauge overall user satisfaction, pinpoint specific positive and negative aspects, and track sentiment shifts over time.
      • Keyword Optimization: Identify frequently used keywords and phrases in reviews to optimize app store listings, improving discoverability and search ranking.
      • Messaging Refinement: Understand how users describe and use the app in their own words, which can inform marketing copy and advertising campaigns.
      • Reputation Management: Monitor rating trends and identify critical reviews quickly to facilitate timely responses and proactive customer engagement.
    • Academic & Data Science Research:

      • Natural Language Processing (NLP): The review and title fields are excellent for training and testing NLP models for sentiment analysis, topic modeling, named entity recognition, and text summarization.
      • User Behavior Analysis: Study patterns in rating distribution, isEdited status, and date to understand user engagement and feedback cycles.
      • Cross-Country Comparisons: Analyze country-specific reviews to understand regional differences in app perception, feature preferences, or cultural nuances in feedback.

    This App Store Reviews dataset provides a direct, unfiltered conduit to understanding user needs and ultimately driving better app performance and greater user satisfaction. Its structured format and granular detail make it an indispensable asset for data-driven decision-making in the mobile app industry.

  3. e

    The manifest and store data of 870,515 Android mobile applications - Dataset...

    • b2find.eudat.eu
    Updated Oct 23, 2023
    + more versions
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    (2023). The manifest and store data of 870,515 Android mobile applications - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/b25ee20e-5268-50ae-9914-4bc70bd4ff1c
    Explore at:
    Dataset updated
    Oct 23, 2023
    Description

    We built a crawler to collect data from the Google Play store including the application's metadata and APK files. The manifest files were extracted from the APK files and then processed to extract the features. The data set is composed of 870,515 records/apps, and for each app we produced 48 features. The data set was used to built and test two bootstrap aggregating of multiple XGBoost machine learning classifiers. The dataset were collected between April 2017 and November 2018. We then checked the status of these applications on three different occasions; December 2018, February 2019, and May-June 2019.

  4. Google Play Store Apps

    • kaggle.com
    Updated Feb 3, 2019
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    Lavanya (2019). Google Play Store Apps [Dataset]. https://www.kaggle.com/lava18/google-play-store-apps/home
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 3, 2019
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Lavanya
    License

    Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
    License information was derived automatically

    Description

    [ADVISORY] IMPORTANT

    Instructions for citation:

    If you use this dataset anywhere in your work, kindly cite as the below: L. Gupta, "Google Play Store Apps," Feb 2019. [Online]. Available: https://www.kaggle.com/lava18/google-play-store-apps

    Context

    While many public datasets (on Kaggle and the like) provide Apple App Store data, there are not many counterpart datasets available for Google Play Store apps anywhere on the web. On digging deeper, I found out that iTunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modern-day techniques (like dynamic page load) using JQuery making scraping more challenging.

    Content

    Each app (row) has values for catergory, rating, size, and more.

    Acknowledgements

    This information is scraped from the Google Play Store. This app information would not be available without it.

    Inspiration

    The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market!

  5. h

    Mobile-Application-Data

    • huggingface.co
    Updated Jun 19, 2023
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    Aaditya s (2023). Mobile-Application-Data [Dataset]. https://huggingface.co/datasets/Aaditya1/Mobile-Application-Data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 19, 2023
    Authors
    Aaditya s
    Description

    Aaditya1/Mobile-Application-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

  6. Z

    Coronavirus-themed Mobile Apps (Malware) Dataset

    • data.niaid.nih.gov
    • explore.openaire.eu
    • +1more
    Updated Apr 21, 2021
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    covid19apps (2021). Coronavirus-themed Mobile Apps (Malware) Dataset [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_3875975
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    Dataset updated
    Apr 21, 2021
    Dataset authored and provided by
    covid19apps
    Description

    As COVID-19 continues to spread across the world, a growing number of malicious campaigns are exploiting the pandemic. It is reported that COVID-19 is being used in a variety of online malicious activities, including Email scam, ransomware and malicious domains. As the number of the afflicted cases continue to surge, malicious campaigns that use coronavirus as a lure are increasing. Malicious developers take advantage of this opportunity to lure mobile users to download and install malicious apps.

    However, besides a few media reports, the coronavirus-themed mobile malware has not been well studied. Our community lacks of the comprehensive understanding of the landscape of the coronavirus-themed mobile malware, and no accessible dataset could be used by our researchers to boost COVID-19 related cybersecurity studies.

    We make efforts to create a daily growing COVID-19 related mobile app dataset. By the time of mid-November, we have curated a dataset of 4,322 COVID-19 themed apps, and 611 of them are considered to be malicious. The number is growing daily and our dataset will update weekly. For more details, please visit https://covid19apps.github.io

    This dataset includes the following files:

    (1) covid19apps.xlsx

    In this file, we list all the COVID-19 themed apps information, including apk file hashes, released date, package name, AV-Rank, etc.

    (2)covid19apps.zip

    We put the COVID-19 themed apps Apk samples in zip files . In order to reduce the size of a single file, we divide the sample into multiple zip files for storage. And the APK file name after the file SHA256.

    If your papers or articles use our dataset, please use the following bibtex reference to cite our paper: https://arxiv.org/abs/2005.14619

    (Accepted to Empirical Software Engineering)

    @misc{wang2021virus, title={Beyond the Virus: A First Look at Coronavirus-themed Mobile Malware}, author={Liu Wang and Ren He and Haoyu Wang and Pengcheng Xia and Yuanchun Li and Lei Wu and Yajin Zhou and Xiapu Luo and Yulei Sui and Yao Guo and Guoai Xu}, year={2021}, eprint={2005.14619}, archivePrefix={arXiv}, primaryClass={cs.CR} }

  7. Data collection among global most privacy demanding mobile iOS apps 2023

    • statista.com
    Updated Jan 16, 2024
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    Statista (2024). Data collection among global most privacy demanding mobile iOS apps 2023 [Dataset]. https://www.statista.com/statistics/1440819/data-collection-most-ios-apps/
    Explore at:
    Dataset updated
    Jan 16, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    May 17, 2023
    Area covered
    Worldwide
    Description

    As of May 2023, Facebook collected the larger number of total unique data points from global iOS users, around 32 data points. Popular digital payment app PayPal and Airbnb collected 26 data points each, while AI tool photo and image editing apps Photoleap collected around 14 unique data points.

  8. IOS App Store reviews dataset

    • crawlfeeds.com
    csv, zip
    Updated Jul 7, 2025
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    Crawl Feeds (2025). IOS App Store reviews dataset [Dataset]. https://crawlfeeds.com/datasets/ios-app-store-reviews-dataset
    Explore at:
    zip, csvAvailable download formats
    Dataset updated
    Jul 7, 2025
    Dataset authored and provided by
    Crawl Feeds
    License

    https://crawlfeeds.com/privacy_policyhttps://crawlfeeds.com/privacy_policy

    Description

    Unlock the power of user feedback with our iOS App Store Reviews Dataset, a comprehensive collection of reviews from thousands of apps across various categories. This robust App Store dataset includes essential details such as app names, ratings, user comments, timestamps, and more, offering valuable insights into user experiences and preferences.

    Perfect for app developers, marketers, and data analysts, this dataset allows you to conduct sentiment analysis, monitor app performance, and identify trends in user behavior. By leveraging the iOS App Store Reviews Dataset, you can refine app features, optimize marketing strategies, and elevate user satisfaction.

    Whether you’re tracking mobile app trends, analyzing specific app categories, or developing data-driven strategies, this App Store dataset is an indispensable tool. Download the iOS App Store Reviews Dataset today or contact us for custom datasets tailored to your unique project requirements.

    Ready to take your app insights to the next level? Get the iOS App Store Reviews Dataset now or explore our custom data solutions to meet your needs.

  9. w

    Dataset of books called Build mobile apps with Ionic 2 and Firebase : hybrid...

    • workwithdata.com
    Updated Apr 17, 2025
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    Work With Data (2025). Dataset of books called Build mobile apps with Ionic 2 and Firebase : hybrid mobile app development [Dataset]. https://www.workwithdata.com/datasets/books?f=1&fcol0=book&fop0=%3D&fval0=Build+mobile+apps+with+Ionic+2+and+Firebase+%3A+hybrid+mobile+app+development
    Explore at:
    Dataset updated
    Apr 17, 2025
    Dataset authored and provided by
    Work With Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset is about books. It has 1 row and is filtered where the book is Build mobile apps with Ionic 2 and Firebase : hybrid mobile app development. It features 7 columns including author, publication date, language, and book publisher.

  10. H

    Worldwide Mobile App User Behavior Dataset

    • dataverse.harvard.edu
    doc, xlsx
    Updated Sep 28, 2014
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    Harvard Dataverse (2014). Worldwide Mobile App User Behavior Dataset [Dataset]. http://doi.org/10.7910/DVN/27459
    Explore at:
    doc(56320), xlsx(7037534)Available download formats
    Dataset updated
    Sep 28, 2014
    Dataset provided by
    Harvard Dataverse
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Time period covered
    2012
    Area covered
    Worldwide
    Description

    We surveyed 10,208 people from more than 15 countries on their mobile app usage behavior. The countries include USA, China, Japan, Germany, France, Brazil, UK, Italy, Russia, India, Canada, Spain, Australia, Mexico, and South Korea. We asked respondents about: (1) their mobile app user behavior in terms of mobile app usage, including the app stores they use, what triggers them to look for apps, why they download apps, why they abandon apps, and the types of apps they download. (2) their demographics including gender, age, marital status, nationality, country of residence, first language, ethnicity, education level, occupation, and household income (3) their personality using the Big-Five personality traits This dataset contains the results of the survey.

  11. b

    App Downloads Data (2025)

    • businessofapps.com
    Updated Aug 1, 2025
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    Business of Apps (2025). App Downloads Data (2025) [Dataset]. https://www.businessofapps.com/data/app-statistics/
    Explore at:
    Dataset updated
    Aug 1, 2025
    Dataset authored and provided by
    Business of Apps
    License

    Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
    License information was derived automatically

    Description

    App Download Key StatisticsApp and Game DownloadsiOS App and Game DownloadsGoogle Play App and Game DownloadsGame DownloadsiOS Game DownloadsGoogle Play Game DownloadsApp DownloadsiOS App...

  12. Multilingual Mobile App Review Dataset August 2025

    • kaggle.com
    Updated Jul 31, 2025
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    Pratyush Puri (2025). Multilingual Mobile App Review Dataset August 2025 [Dataset]. https://www.kaggle.com/datasets/pratyushpuri/multilingual-mobile-app-reviews-dataset-2025
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 31, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Pratyush Puri
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    Multilingual Mobile App Reviews Dataset 2025

    Overview

    This comprehensive synthetic dataset contains 2,514 authentic mobile app reviews spanning 40+ popular applications across 24 different languages, making it ideal for multilingual NLP, sentiment analysis, and cross-cultural user behavior research.

    Dataset Statistics

    • Total Records: 2,514 reviews
    • Columns: 15 features
    • Languages Covered: 24 international languages
    • Apps Included: 40+ popular mobile applications
    • Time Range: 2023-2025 (2-year span)
    • File Format: CSV
    • Data Quality: Intentionally includes missing values and mixed data types for data cleaning practice

    Column Specifications

    Column NameData TypeDescriptionSample ValuesNull Count
    review_idIntegerUnique identifier for each review1, 2, 3, ...0
    user_idString*User identifier (should be integer)"1967825", "9242600"0
    app_nameStringName of the mobile applicationWhatsApp, Instagram, TikTok0
    app_categoryStringApplication categorySocial Networking, Entertainment0
    review_textStringMultilingual review content"This app is amazing!"63
    review_languageStringISO language codeen, es, fr, zh, hi, ar0
    ratingMixed*App rating (1.0-5.0, some as strings)4.5, "3.2", 1.138
    review_dateDateTimeTimestamp of review submission2024-10-09 19:26:400
    verified_purchaseBooleanPurchase verification statusTrue, False0
    device_typeStringDevice platformAndroid, iOS, iPad, Windows Phone0
    num_helpful_votesMixed*Helpfulness votes (some as strings)65, "209", 1630
    user_ageFloat*User age (should be integer)14.0, 18.0, 67.00
    user_countryStringUser's countryChina, Germany, Nigeria50
    user_genderStringUser genderMale, Female, Non-binary, Prefer not to say88
    app_versionStringApplication version number1.4, v8.9, 2.8.37.592625

    Note: Data types marked with asterisk require cleaning/conversion

    Language Distribution

    The dataset includes reviews in 24 languages: - European: English (en), Spanish (es), French (fr), German (de), Italian (it), Russian (ru), Polish (pl), Dutch (nl), Swedish (sv), Danish (da), Norwegian (no), Finnish (fi) - Asian: Chinese (zh), Hindi (hi), Japanese (ja), Korean (ko), Thai (th), Vietnamese (vi), Indonesian (id), Malay (ms) - Other: Arabic (ar), Turkish (tr), Filipino (tl)

    Application Categories

    Reviews cover 18 distinct categories: - Social Networking - Entertainment
    - Productivity - Travel & Local - Music & Audio - Video Players & Editors - Shopping - Navigation - Finance - Communication - Education - Photography - Dating - Business - Utilities - Health & Fitness - Games - News & Magazines

    Popular Apps Included

    40+ applications including: - Social: WhatsApp, Instagram, Facebook, Snapchat, TikTok, LinkedIn, Twitter, Reddit, Pinterest - Entertainment: YouTube, Netflix, Spotify - Productivity: Microsoft Office, Google Drive, Dropbox, OneDrive, Zoom, Discord - Travel: Uber, Lyft, Airbnb, Booking.com, Google Maps, Waze - Finance: PayPal, Venmo - Education: Duolingo, Khan Academy, Coursera, Udemy - Tools: Grammarly, Canva, Adobe Photoshop, VLC, MX Player

    Geographic Distribution

    Reviews from 24 countries across all continents: - Asia: China, India, Japan, South Korea, Thailand, Vietnam, Indonesia, Malaysia, Philippines, Pakistan, Bangladesh - Europe: Germany, United Kingdom, France, Italy, Spain, Russia, Turkey, Poland - Americas: United States, Canada, Brazil, Mexico - Oceania: Australia - Africa: Nigeria

    Data Quality Features

    Intentional data challenges for learning: - Missing Values: Strategic nulls in review_text (63), rating (38), user_country (50), user_gender (88), app_version (25) - Data Type Issues: - user_id stored as strings (should be integers) - user_age as floats (should be integers)
    - Some ratings as strings (should be floats) - Some helpful_votes as strings (should be integers) - Mixed Version Formats: "1.4", "v8.9", "2.8.37.5926", "14.1.60.318-beta"

    Use Cases

    This dataset is perfect for: - Multilingual NLP projects and sentiment analysis - Cross-cultural user behavior analysis - App store analytics and rating prediction - Data cleaning and preprocessing practice - Text classification across multiple languages - Time series analysis of app reviews - Geographic sentiment analysis - Data engineering pipeline development

    Data Cleaning Opportunities

    • Convert string IDs to integers
    • Standardize rating values to float
    • Han...
  13. Data from: Testing of Mobile Applications in the Wild: A Large-Scale...

    • figshare.com
    txt
    Updated Mar 25, 2020
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    Fabiano Pecorelli (2020). Testing of Mobile Applications in the Wild: A Large-Scale Empirical Study on Android Apps [Dataset]. http://doi.org/10.6084/m9.figshare.9980672.v1
    Explore at:
    txtAvailable download formats
    Dataset updated
    Mar 25, 2020
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Fabiano Pecorelli
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Nowadays, mobile applications (a.k.a., apps) are used by over two billion users for every type of need, including social and emergency connectivity. Their pervasiveness in today world has inspired the software testing research community in devising approaches to allow developers to better test their apps and improve the quality of the tests being developed. In spite of this research effort, we still notice a lack of empirical analyses aiming at assessing the actual quality of test cases manually developed by mobile developers: this perspective could provide evidence-based findings on the future research directions in the field as well as on the current status of testing in the wild. As such, we performed a large-scale empirical study targeting 1,780 open-source Android apps and aiming at assessing (1) the extent to which these apps are actually tested, (2) how well-designed are the available tests, and (3) what is their effectiveness. The key results of our study show that mobile developers still tend not to properly test their apps, possibly because of time to market requirements. Furthermore, we discovered that the test cases of the considered apps have a low (i) design quality, both in terms of test code metrics and test smells, and (ii) effectiveness when considering code coverage as well as assertion density.

  14. Apps in selected categories collecting data types from global iOS users 2023...

    • statista.com
    Updated Jan 16, 2024
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    Statista (2024). Apps in selected categories collecting data types from global iOS users 2023 [Dataset]. https://www.statista.com/statistics/1440894/ios-apps-in-selected-category-collecting-data/
    Explore at:
    Dataset updated
    Jan 16, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    May 17, 2023
    Area covered
    Worldwide
    Description

    As of May 2023, product interaction data were the most commonly collected data points, with 94 over the 100 analyzed apps reporting to collect such data. User ID and crash data were collected by by 93 and 92 apps over 100, respectively. Over the 10 leading shopping apps hosted on the Apple App Store, the totality collected precise location, physical address, and payment info.

  15. R

    Object Detection Mobile App Dataset

    • universe.roboflow.com
    zip
    Updated Jul 15, 2025
    + more versions
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    RS Workspace (2025). Object Detection Mobile App Dataset [Dataset]. https://universe.roboflow.com/rs-workspace-gnusl/object-detection-mobile-app
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jul 15, 2025
    Dataset authored and provided by
    RS Workspace
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Objects Bounding Boxes
    Description

    Object Detection Mobile App

    ## Overview
    
    Object Detection Mobile App is a dataset for object detection tasks - it contains Objects annotations for 2,255 images.
    
    ## Getting Started
    
    You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
    
      ## License
    
      This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
    
  16. IOS application reviews dataset in English

    • crawlfeeds.com
    csv, zip
    Updated Jul 8, 2025
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    Crawl Feeds (2025). IOS application reviews dataset in English [Dataset]. https://crawlfeeds.com/datasets/ios-application-reviews-dataset-in-english
    Explore at:
    zip, csvAvailable download formats
    Dataset updated
    Jul 8, 2025
    Dataset authored and provided by
    Crawl Feeds
    License

    https://crawlfeeds.com/privacy_policyhttps://crawlfeeds.com/privacy_policy

    Description

    This comprehensive iOS application reviews dataset contains thousands of authentic user reviews from the Apple App Store in English. The dataset provides valuable insights for app developers, marketers, and researchers studying mobile application performance and user sentiment.

    Key Features:

    • Real user reviews from popular iOS apps
    • Star ratings from 1 to 5 stars
    • Review dates and timestamps
    • App store URLs and metadata
    • User demographics and location data
    • App version information
    • Review titles and detailed feedback

    Applications: Perfect for sentiment analysis, app store optimization, mobile app development research, user experience studies, and competitive analysis. This dataset enables businesses to understand user preferences, identify app improvement opportunities, and develop better mobile applications.

    Data Quality: All reviews are genuine user feedback collected from the official Apple App Store, ensuring authenticity and reliability for research and business intelligence purposes. The dataset covers various app categories including fitness, shopping, education, entertainment, and productivity applications.

  17. Data collected from global educational apps 2022

    • statista.com
    Updated Jul 9, 2025
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    Statista (2025). Data collected from global educational apps 2022 [Dataset]. https://www.statista.com/statistics/1381230/data-points-collected-education-apps/
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    Dataset updated
    Jul 9, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    As of August 2022, language learning app HelloTalk and Google's meeting point for schools Google Classroom were the educational app collecting the largest amount of data points. ClassDojo and popular language learning app Duolingo followed, collecting approximately ** different data points from global Android users.

  18. b

    Google Play Store Datasets

    • brightdata.com
    .json, .csv, .xlsx
    Updated Apr 11, 2024
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    Bright Data (2024). Google Play Store Datasets [Dataset]. https://brightdata.com/products/datasets/google-play-store
    Explore at:
    .json, .csv, .xlsxAvailable download formats
    Dataset updated
    Apr 11, 2024
    Dataset authored and provided by
    Bright Data
    License

    https://brightdata.com/licensehttps://brightdata.com/license

    Area covered
    Worldwide
    Description

    This dataset encompasses a wide-ranging collection of Google Play applications, providing a holistic view of the diverse ecosystem within the platform. It includes information on various attributes such as the title, developer, monetization features, images, app descriptions, data safety measures, user ratings, number of reviews, star rating distributions, user feedback, recent updates, related applications by the same developer, content ratings, estimated downloads, and timestamps. By aggregating this data, the dataset offers researchers, developers, and analysts an extensive resource to explore and analyze trends, patterns, and dynamics within the Google Play Store. Researchers can utilize this dataset to conduct comprehensive studies on user behavior, market trends, and the impact of various factors on app success. Developers can leverage the insights derived from this dataset to inform their app development strategies, improve user engagement, and optimize monetization techniques. Analysts can employ the dataset to identify emerging trends, assess the performance of different categories of applications, and gain valuable insights into consumer preferences. Overall, this dataset serves as a valuable tool for understanding the broader landscape of the Google Play Store and unlocking actionable insights for various stakeholders in the mobile app industry.

  19. b

    US App Market Statistics (2025)

    • businessofapps.com
    Updated Sep 5, 2024
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    Business of Apps (2024). US App Market Statistics (2025) [Dataset]. https://www.businessofapps.com/data/us-app-market/
    Explore at:
    Dataset updated
    Sep 5, 2024
    Dataset authored and provided by
    Business of Apps
    License

    Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
    License information was derived automatically

    Description

    Key US App Market StatisticsUS App Market SizeUS App Market Revenue by AppUS Smartphone UsersUS Smartphone PopulationTime Spent on Apps in the USUS App Market DownloadsUS Downloads by AppUS Daily...

  20. i

    Encrypted communication dataset for mobile applications

    • ieee-dataport.org
    Updated May 26, 2022
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    Daniel Snasel (2022). Encrypted communication dataset for mobile applications [Dataset]. https://ieee-dataport.org/documents/encrypted-communication-dataset-mobile-applications
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    Dataset updated
    May 26, 2022
    Authors
    Daniel Snasel
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    the device was left without user interaction for 5 minutes.

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Onur Gunes (2021). RICO dataset [Dataset]. https://www.kaggle.com/datasets/onurgunes1993/rico-dataset
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RICO dataset

A Mobile App Dataset for Building Data-Driven Design Applications

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Dec 2, 2021
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Onur Gunes
Description

Context

Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. The Rico dataset contains design data from more than 9.3k Android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 66k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports query-by-example search over UIs.

Content

Rico was built by mining Android apps at runtime via human-powered and programmatic exploration. Like its predecessor ERICA, Rico’s app mining infrastructure requires no access to — or modification of — an app’s source code. Apps are downloaded from the Google Play Store and served to crowd workers through a web interface. When crowd workers use an app, the system records a user interaction trace that captures the UIs visited and the interactions performed on them. Then, an automated agent replays the trace to warm up a new copy of the app and continues the exploration programmatically, leveraging a content-agnostic similarity heuristic to efficiently discover new UI states. By combining crowdsourcing and automation, Rico can achieve higher coverage over an app’s UI states than either crawling strategy alone. In total, 13 workers recruited on UpWork spent 2,450 hours using apps on the platform over five months, producing 10,811 user interaction traces. After collecting a user trace for an app, we ran the automated crawler on the app for one hour.

Acknowledgements

UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN https://interactionmining.org/rico

Inspiration

The Rico dataset is large enough to support deep learning applications. We trained an autoencoder to learn an embedding for UI layouts, and used it to annotate each UI with a 64-dimensional vector representation encoding visual layout. This vector representation can be used to compute structurally — and often semantically — similar UIs, supporting example-based search over the dataset. To create training inputs for the autoencoder that embed layout information, we constructed a new image for each UI capturing the bounding box regions of all leaf elements in its view hierarchy, differentiating between text and non-text elements. Rico’s view hierarchies obviate the need for noisy image processing or OCR techniques to create these inputs.

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