research-article

Android apps and user feedback: a dataset for software evolution and quality improvement

Authors:

Giovanni Grano,

Andrea Di Sorbo,

Francesco Mercaldo,

Corrado A. Visaggio,

Gerardo Canfora,

Sebastiano PanichellaAuthors Info & Claims

WAMA 2017: Proceedings of the 2nd ACM SIGSOFT International Workshop on App Market Analytics

Pages 8 - 11

https://doi.org/10.1145/3121264.3121266

Published: 05 September 2017 Publication History

Abstract

Nowadays, Android represents the most popular mobile platform with a market share of around 80%. Previous research showed that data contained in user reviews and code change history of mobile apps represent a rich source of information for reducing software maintenance and development effort, increasing customers' satisfaction. Stemming from this observation, we present in this paper a large dataset of Android applications belonging to 23 different apps categories, which provides an overview of the types of feedback users report on the apps and documents the evolution of the related code metrics. The dataset contains about 395 applications of the F-Droid repository, including around 600 versions, 280,000 user reviews and more than 450,000 user feedback (extracted with specific text mining approaches). Furthermore, for each app version in our dataset, we employed the Paprika tool and developed several Python scripts to detect 8 different code smells and compute 22 code quality indicators. The paper discusses the potential usefulness of the dataset for future research in the field.

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Abstract 1 Introduction 2 Related Work 3 Dataset Construction 3.1 Data Collection Phase 3.2 Analysis Phase 4 Analytics & Data Sharing 5 Conclusions References

Cited By

Coppola RFulcini TArdito LTorchiano M(2025)Kotlin assimilating the Android ecosystem: An appraisal of diffusion and impact on maintainabilityJournal of Systems and Software10.1016/j.jss.2025.112346222(112346)Online publication date: Apr-2025
https://doi.org/10.1016/j.jss.2025.112346
Assunção GIzbicki RPrates M(2024)Is Augmentation Effective in Improving Prediction in Imbalanced Datasets?Journal of Data Science10.6339/24-JDS1154(1-16)Online publication date: 15-Oct-2024
https://doi.org/10.6339/24-JDS1154
Halit Yilmaz EHakki Toroslu İKöksal Ö(2024)A Comparative Study of Contemporary Learning Paradigms in Bug Report Priority DetectionIEEE Access10.1109/ACCESS.2024.345112512(126577-126586)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3451125
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Index Terms

Android apps and user feedback: a dataset for software evolution and quality improvement
1. Software and its engineering
  1. Software creation and management
    1. Software development process management
    2. Software post-development issues
      1. Software evolution

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cover image ACM Conferences

WAMA 2017: Proceedings of the 2nd ACM SIGSOFT International Workshop on App Market Analytics

September 2017

25 pages

ISBN:9781450351584

DOI:10.1145/3121264

Copyright © 2017 ACM.

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 ACM 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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SIGSOFT: ACM Special Interest Group on Software Engineering

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 September 2017

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ESEC/FSE'17

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SIGSOFT

ESEC/FSE'17: Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering

September 5, 2017

Paderborn, Germany

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Cited By

Coppola RFulcini TArdito LTorchiano M(2025)Kotlin assimilating the Android ecosystem: An appraisal of diffusion and impact on maintainabilityJournal of Systems and Software10.1016/j.jss.2025.112346222(112346)Online publication date: Apr-2025
https://doi.org/10.1016/j.jss.2025.112346
Assunção GIzbicki RPrates M(2024)Is Augmentation Effective in Improving Prediction in Imbalanced Datasets?Journal of Data Science10.6339/24-JDS1154(1-16)Online publication date: 15-Oct-2024
https://doi.org/10.6339/24-JDS1154
Halit Yilmaz EHakki Toroslu İKöksal Ö(2024)A Comparative Study of Contemporary Learning Paradigms in Bug Report Priority DetectionIEEE Access10.1109/ACCESS.2024.345112512(126577-126586)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3451125
Wohllebe AWohllebe A(2024)Überblick: Charakterisierung von Apps und Erwartungen von NutzernPraxisguide App-Marketing10.1007/978-3-658-42981-2_3(87-161)Online publication date: 1-Feb-2024
https://doi.org/10.1007/978-3-658-42981-2_3
Kögler MPaulick KScheffran JBirkholz M(2024)Sustainable use of a smartphone and regulatory needsSustainable Development10.1002/sd.299532:6(6182-6200)Online publication date: 29-Apr-2024
https://doi.org/10.1002/sd.2995
Wu ZChen XLee S(2023)A systematic literature review on Android-specific smellsJournal of Systems and Software10.1016/j.jss.2023.111677201(111677)Online publication date: Jul-2023
https://doi.org/10.1016/j.jss.2023.111677
Alshangiti MShi WLima ELiu XYu QRoychoudhury ACadar CKim M(2022)Hierarchical Bayesian multi-kernel learning for integrated classification and summarization of app reviewsProceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3540250.3549174(558-569)Online publication date: 7-Nov-2022
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Elish KElish MAlmohri H(2022)Lightweight, Effective Detection and Characterization of Mobile Malware FamiliesIEEE Transactions on Computers10.1109/TC.2022.314343971:11(2982-2995)Online publication date: 1-Nov-2022
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Mojica-Hanke ABello-Jimenez LEscobar-Velasquez CLinares-Vasquez M(2022)Crème de la crème. Investigating Metadata and Survivability of Top Android Apps2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER53432.2022.00064(469-480)Online publication date: Mar-2022
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Chand RRehman Khan SHussain SWang W(2022)TTAG+R: A Dataset of Google Play Store's Top Trending Android Games and User Reviews2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)10.1109/QRS-C57518.2022.00093(580-586)Online publication date: Dec-2022
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