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  1. Mobile Apps Issues

    • kaggle.com
    zip
    Updated Mar 29, 2024
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    Wael Shaher (2024). Mobile Apps Issues [Dataset]. https://www.kaggle.com/datasets/waelshaher/mobile-apps-issues
    Explore at:
    zip(1420150 bytes)Available download formats
    Dataset updated
    Mar 29, 2024
    Authors
    Wael Shaher
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Dataset

    This dataset was created by Wael Shaher

    Released under CC0: Public Domain

    Contents

  2. Mobile Apps ScreenTime Analysis

    • kaggle.com
    Updated Dec 31, 2024
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    ANAND SHAW (2024). Mobile Apps ScreenTime Analysis [Dataset]. https://www.kaggle.com/datasets/anandshaw2001/mobile-apps-screentime-analysis
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 31, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    ANAND SHAW
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Don't forget to hit the upvote🙏

    This DataSet Contains Detailed Insights into Mobile App Usage Patterns, including ScreenTime, notifications received, and app openings. The data spans multiple days in August and some popular apps, offering a granular view of digital behavior.

    Features:

    1. Date: The date of the recorded data.

    2. App: The name of the mobile application.

    3. Usage (minutes): Total minutes spent using the app on a given day.

    4. Notifications: Number of notifications received from the app.

    5. Times Opened: How many times the app was launched.

  3. H

    Worldwide Mobile App User Behavior Dataset

    • dataverse.harvard.edu
    Updated Sep 28, 2014
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    Soo Ling Lim (2014). Worldwide Mobile App User Behavior Dataset [Dataset]. http://doi.org/10.7910/DVN/27459
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 28, 2014
    Dataset provided by
    Harvard Dataverse
    Authors
    Soo Ling Lim
    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.

  4. d

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

    • b2find.dkrz.de
    Updated May 6, 2023
    + more versions
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    (2023). The manifest and store data of 870,515 Android mobile applications - Dataset - B2FIND [Dataset]. https://b2find.dkrz.de/dataset/b25ee20e-5268-50ae-9914-4bc70bd4ff1c
    Explore at:
    Dataset updated
    May 6, 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.

  5. Z

    Coronavirus-themed Mobile Apps (Malware) Dataset

    • data.niaid.nih.gov
    • zenodo.org
    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
    Explore at:
    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} }

  6. App Store Reviews for a Mobile App

    • kaggle.com
    Updated Sep 29, 2024
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    Anil (2024). App Store Reviews for a Mobile App [Dataset]. https://www.kaggle.com/datasets/sanlian/app-store-reviews-for-a-mobile-app
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 29, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Anil
    License

    http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/

    Description

    This dataset contains fictional reviews from a hypothetical mobile application, generated for demo purposes in various projects. The reviews include detailed feedback from users across different countries and platforms, with additional attributes such as star ratings, like/dislike counts, and issue flags. The data was later used as an input for a large language model (LLM) to generate labeled outputs, which are included in a separate dataset named labeled_app_store_reviews. This labeled dataset can be used for machine learning tasks such as sentiment analysis, text classification, or even A/B testing simulations.

  7. P

    UI5k Dataset

    • paperswithcode.com
    Updated Jul 10, 2020
    + more versions
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    (2020). UI5k Dataset [Dataset]. https://paperswithcode.com/dataset/ui5k
    Explore at:
    Dataset updated
    Jul 10, 2020
    Description

    This dataset contains 54,987 UI screenshots and the metadata from 7,748 Android applications belonging to 25 application categories

    Download link: https://www.dropbox.com/sh/kfkhevxykzwputb/AAAhL6ipmOg4zZn4jUL_myF0a?dl=0

  8. b

    Data from: Google Play Store Datasets

    • brightdata.com
    .json, .csv, .xlsx
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    Bright Data, Google Play Store Datasets [Dataset]. https://brightdata.com/products/datasets/google-play-store
    Explore at:
    .json, .csv, .xlsxAvailable download formats
    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.

  9. m

    User Reviews of BCA Mobile App from Google Play Store (December 2023 - June...

    • data.mendeley.com
    Updated Jun 14, 2024
    + more versions
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    Martinus Juan Prasetyo (2024). User Reviews of BCA Mobile App from Google Play Store (December 2023 - June 2024) [Dataset]. http://doi.org/10.17632/mvshyj7g67.1
    Explore at:
    Dataset updated
    Jun 14, 2024
    Authors
    Martinus Juan Prasetyo
    License

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

    Description

    This dataset comprises 10,000 user reviews of the BCA Mobile app collected from the Google Play Store between December 24, 2023, and June 12, 2024. Each review includes the user's name, the rating they provided (ranging from 1 to 5 stars), the timestamp of when the review was created, and the text content of the review. The dataset is in Indonesian and focuses on feedback from users in Indonesia. This data can be used to perform sentiment analysis, understand user experiences, identify common issues, and assess the overall performance of the BCA Mobile app during the specified timeframe. The reviews are sorted based on the newest first, providing the latest feedback at the top.

  10. i

    LSApp: Large dataset of Sequential mobile App usage

    • ieee-dataport.org
    Updated Feb 24, 2025
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    Mohammad Aliannejadi (2025). LSApp: Large dataset of Sequential mobile App usage [Dataset]. http://doi.org/10.21227/w17r-xx75
    Explore at:
    Dataset updated
    Feb 24, 2025
    Dataset provided by
    IEEE Dataport
    Authors
    Mohammad Aliannejadi
    License

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

    Description

    During the study period, with the help of 292 participants, we were able to collect 599,635 app usage records. Here, we summarize the main characteristics of the participants based on the submitted surveys. 59% of the participants were female and 50% aged between 25 and 34. Participants were from all kinds of educational backgrounds ranging from high school diploma to PhD. In particular, 32% of them had a college degree, followed by 30% with a bachelor's degree. Smartphone was the main device used for connecting to the Internet for 53% of the participants, followed by laptop (25%). Among the participants, 67% used their smartphones more often for personal reasons rather than work. Finally, half of the participants stated that they use their smartphones 4 hours a day or more

  11. d

    Hawaii.gov Mobile Apps

    • catalog.data.gov
    • opendata.hawaii.gov
    Updated Apr 10, 2024
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    Other (2024). Hawaii.gov Mobile Apps [Dataset]. https://catalog.data.gov/dataset/hawaii-gov-mobile-apps
    Explore at:
    Dataset updated
    Apr 10, 2024
    Dataset provided by
    Other
    Area covered
    Hawaii
    Description

    Mobile Apps for the state of Hawaii

  12. 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

  13. c

    IOS App Store reviews dataset

    • crawlfeeds.com
    json, zip
    Updated Jan 21, 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:
    json, zipAvailable download formats
    Dataset updated
    Jan 21, 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.

  14. Z

    User Feedback Dataset from the Top 15 Downloaded Mobile Applications

    • data.niaid.nih.gov
    • zenodo.org
    Updated Nov 24, 2023
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    hendrawati, Triyani (2023). User Feedback Dataset from the Top 15 Downloaded Mobile Applications [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_10204231
    Explore at:
    Dataset updated
    Nov 24, 2023
    Dataset provided by
    hendrawati, Triyani
    Asnawi, Mohammad Hamid
    Herawan, Tutut
    Pravitasari, Anindya Apriliyanti
    License

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

    Description

    This dataset comprises user feedback data collected from 15 globally acclaimed mobile applications, spanning diverse categories. The included applications are among the most downloaded worldwide, providing a rich and varied source for analysis. The dataset is particularly suitable for Natural Language Processing (NLP) applications, such as text classification and topic modeling. List of Included Applications:

    TikTok Instagram Facebook WhatsApp Telegram Zoom Snapchat Facebook Messenger Capcut Spotify YouTube HBO Max Cash App Subway Surfers Roblox Data Columns and Descriptions: Data Columns and Descriptions:

    review_id: Unique identifiers for each user feedback/application review. content: User-generated feedback/review in text format. score: Rating or star given by the user. TU_count: Number of likes/thumbs up (TU) received for the review. app_id: Unique identifier for each application. app_name: Name of the application. RC_ver: Version of the app when the review was created (RC). Terms of Use: This dataset is open access for scientific research and non-commercial purposes. Users are required to acknowledge the authors' work and, in the case of scientific publication, cite the most appropriate reference: M. H. Asnawi, A. A. Pravitasari, T. Herawan, and T. Hendrawati, "The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling," in IEEE Access, vol. 11, pp. 130272-130286, 2023, doi: 10.1109/ACCESS.2023.3332644.

    Researchers and analysts are encouraged to explore this dataset for insights into user sentiments, preferences, and trends across these top mobile applications. If you have any questions or need further information, feel free to contact the dataset authors.

  15. Dataset about user privacy treatment by mobile applications

    • zenodo.org
    • data.niaid.nih.gov
    zip
    Updated Nov 9, 2020
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    Molina L.M.; Molina L.M. (2020). Dataset about user privacy treatment by mobile applications [Dataset]. http://doi.org/10.5281/zenodo.4261664
    Explore at:
    zipAvailable download formats
    Dataset updated
    Nov 9, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Molina L.M.; Molina L.M.
    License

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

    Description
    With academical purposes for the Master in Data Science at UOC, this data extraction project is carried out using Web Scraping techniques on the Exodus-Privacy website, which is dedicated to analyze security and privacy aspects in Android applications. The dataset about user privacy treatment by mobile applications, provides information on trackers that have been included in the application and the device permissions that the user must accept at the time of installation. In addition, it provides more interesting application features for analytical processing of mobile applications.
    
    Dataframe files:
     · exodus.zip: Contains de icon attribute within the dataset file exodus.json (3G) in a [RGBA] 32x32 list format.
     · exodusNoIcon.zip: Contains de dataset file exodusNoIcon.json (100M) with 153.373 png files. Each file is named with the Id attribute within the dataset file.
    
    Dataframe attributes:
    {
      "id": {
        "Id": id,
        "Name": "name",
        "Tracker_count": trackersCount,
        "Permissions_count": permissionsCount,
        "Version": "version",
        "Downloads": "downloads",
        "Analysis_date": "analysisDate",
        "Trackers": [
          {
            "Tracker Name": [
              "trackerPurpose"
            ]
          }
        ],
        "Permissions": [
          "permission",
        ],
        "Permissions_warning_count": permissionWarningCount,
        "Developer": "developer",
        "Country": "country",
        "Icon": [
          [
            R,
            G,
            B,
            A
          ]
        ]
      }
    }

  16. Smartphone Usage and Behavioral Dataset

    • kaggle.com
    Updated Oct 23, 2024
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    Bhadra Mohit (2024). Smartphone Usage and Behavioral Dataset [Dataset]. https://www.kaggle.com/datasets/bhadramohit/smartphone-usage-and-behavioral-dataset/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 23, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Bhadra Mohit
    License

    https://cdla.io/sharing-1-0/https://cdla.io/sharing-1-0/

    Description

    Context

    This dataset provides insights into the daily mobile usage patterns of 1,000 users, covering aspects such as screen time, app usage, and user engagement across different app categories.

    It includes a diverse range of users based on age, gender, and location.

    The data focuses on total app usage, time spent on social media, productivity, and gaming apps, along with overall screen time.

    This information is valuable for understanding behavioral trends and app usage preferences, making it useful for app developers, marketers, and UX researchers.

    This dataset is useful for analyzing mobile engagement, app usage habits, and the impact of demographic factors on mobile behavior. It can help identify trends for marketing, app development, and user experience optimization.

    Outcome

    This dataset enables a deeper understanding of mobile user behavior and app engagement across different demographics.

    Key outcomes include insights into app usage preferences, daily screen time habits, and the impact of age, gender, and location on mobile behavior.

    This analysis can help identify patterns for improving user experience, tailoring marketing strategies, and optimizing app development for different user segments.

  17. 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
    figshare
    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.

  18. t

    Mobile App Usage Log Data - Dataset - LDM

    • service.tib.eu
    Updated Jan 2, 2025
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    (2025). Mobile App Usage Log Data - Dataset - LDM [Dataset]. https://service.tib.eu/ldmservice/dataset/mobile-app-usage-log-data
    Explore at:
    Dataset updated
    Jan 2, 2025
    Description

    Mobile app usage log data used to mine contextual behavioral rules of individual mobile phone users

  19. m

    ITC-Net-Blend-60: A Comprehensive Dataset for Robust Mobile App...

    • data.mendeley.com
    Updated Nov 15, 2023
    + more versions
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    Marziyeh Bayat (2023). ITC-Net-Blend-60: A Comprehensive Dataset for Robust Mobile App Identification in Real-World Network Environment - Scenario A [Dataset]. http://doi.org/10.17632/ssv23kfcgs.1
    Explore at:
    Dataset updated
    Nov 15, 2023
    Authors
    Marziyeh Bayat
    License

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

    Area covered
    World
    Description

    This dataset includes network traffic data from more than 50 Android applications across 5 different scenarios. The applications are consistent in all scenarios, but other factors like location, device, and user vary (see Table 2 in the paper). The current repository pertains to Scenario A. Within the repository, for each application, there is a compressed file containing the relevant PCAP files. The PCAP files follow the naming convention: {Application Name}{Scenario ID}{#Trace}_Final.pcap.

  20. Data from: AndroR2: A Dataset of Manually-Reproduced Bug Reports for Android...

    • zenodo.org
    • data.niaid.nih.gov
    application/gzip, bin
    Updated Mar 31, 2021
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    Tyler Wendland; Jingyang Sun; Junayed Mahmud; SM Hasan Mansur; Steven Huang; Kevin Moran; Julia Rubin; Mattia Fazzini; Tyler Wendland; Jingyang Sun; Junayed Mahmud; SM Hasan Mansur; Steven Huang; Kevin Moran; Julia Rubin; Mattia Fazzini (2021). AndroR2: A Dataset of Manually-Reproduced Bug Reports for Android apps [Dataset]. http://doi.org/10.5281/zenodo.4646313
    Explore at:
    bin, application/gzipAvailable download formats
    Dataset updated
    Mar 31, 2021
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Tyler Wendland; Jingyang Sun; Junayed Mahmud; SM Hasan Mansur; Steven Huang; Kevin Moran; Julia Rubin; Mattia Fazzini; Tyler Wendland; Jingyang Sun; Junayed Mahmud; SM Hasan Mansur; Steven Huang; Kevin Moran; Julia Rubin; Mattia Fazzini
    License

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

    Description

    AndroR2 is a dataset of 90 manually reproduced bug reports for Android apps listed on Google Play and hosted on GitHub, systematically collected via an in-depth analysis of 459 reports extracted from the GitHub issue tracker. For each reproduced report, AndroR2 includes the original bug report, an apk file for the buggy version of the app, an executable reproduction script, and metadata regarding the quality of the reproduction steps associated with the original report. We believe that the AndroR2 dataset can be used to facilitate research in automatically analyzing, understanding, reproducing, localizing, and fixing bugs for mobile applications as well as other software maintenance activities more broadly.

Share
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Wael Shaher (2024). Mobile Apps Issues [Dataset]. https://www.kaggle.com/datasets/waelshaher/mobile-apps-issues
Organization logo

Mobile Apps Issues

Consist from 5 classes of Mobile issues

Explore at:
39 scholarly articles cite this dataset (View in Google Scholar)
zip(1420150 bytes)Available download formats
Dataset updated
Mar 29, 2024
Authors
Wael Shaher
License

https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

Description

Dataset

This dataset was created by Wael Shaher

Released under CC0: Public Domain

Contents