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A graph-based dataset of commit history of real-world Android apps

Published: 28 May 2018 Publication History

Abstract

Obtaining a good dataset to conduct empirical studies on the engineering of Android apps is an open challenge. To start tackling this challenge, we present AndroidTimeMachine, the first, self-contained, publicly available dataset weaving spread-out data sources about real-world, open-source Android apps. Encoded as a graph-based database, AndroidTimeMachine concerns 8,431 real open-source Android apps and contains: (i) metadata about the apps' GitHub projects, (ii) Git repositories with full commit history and (iii) metadata extracted from the Google Play store, such as app ratings and permissions.

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    cover image ACM Conferences
    MSR '18: Proceedings of the 15th International Conference on Mining Software Repositories
    May 2018
    627 pages
    ISBN:9781450357166
    DOI:10.1145/3196398
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 28 May 2018

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    Author Tags

    1. Android
    2. dataset
    3. mining software repositories

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    • Swiss National Science Foundation

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    View all
    • (2023)Fingerprinting and Building Large Reproducible DatasetsProceedings of the 2023 ACM Conference on Reproducibility and Replicability10.1145/3589806.3600043(27-36)Online publication date: 27-Jun-2023
    • (2023)Detecting Backdoors in Collaboration Graphs of Software RepositoriesProceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy10.1145/3577923.3583657(189-200)Online publication date: 24-Apr-2023
    • (2023)Learning migration models for supporting incremental language migrations of software applicationsInformation and Software Technology10.1016/j.infsof.2022.107082153:COnline publication date: 1-Jan-2023
    • (2023)A community detection approach based on network representation learning for repository miningExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120597231:COnline publication date: 30-Nov-2023
    • (2023)Fitting missing API puzzles with machine translation techniquesExpert Systems with Applications10.1016/j.eswa.2022.119477216(119477)Online publication date: Apr-2023
    • (2023)JEMMA: An extensible Java dataset for ML4Code applicationsEmpirical Software Engineering10.1007/s10664-022-10275-728:2Online publication date: 10-Mar-2023
    • (2022)Algorithms, Social Rejection, and Public Administrations in the Current WorldHandbook of Research on Artificial Intelligence in Government Practices and Processes10.4018/978-1-7998-9609-8.ch005(66-86)Online publication date: 2022
    • (2022)Adoption of third-party libraries in mobile appsProceedings of the 9th IEEE/ACM International Conference on Mobile Software Engineering and Systems10.1145/3524613.3527810(125-135)Online publication date: 17-May-2022
    • (2022)Why Did Developers Migrate Android Applications From Java to Kotlin?IEEE Transactions on Software Engineering10.1109/TSE.2021.312036748:11(4521-4534)Online publication date: 1-Nov-2022
    • (2022)Detecting Connectivity Issues in Android Apps2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER53432.2022.00087(697-708)Online publication date: Mar-2022
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