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.

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.

A large-scale dataset on the dynamic profiles based on function calls of 35,974 benign and malicious Android apps from 10 historical years (2010 through 2019). Function calls are a commonly used means to model program behaviors, which may contribute to various code analysis approaches to assuring software correctness, reliability, and security. In particular, our dataset includes dynamic profiles of each app resulting from the same-length of time (10 mins) of being exercised by randomly generated inputs on both emulator and real device, enabling interesting and useful app analysis that reason about app behaviors in an evolutionary perspective while informing the differences of app behaviors on different run-time hardware platforms. Since we have 20 yearly datasets associated with 35,974 unique Android apps across the 10 years, profiling these apps took 12,000 hours. Considering the costs of filtering out apps that were originally sampled but that we were unable to profile (due to various reasons such as broken APKs, not being executable because of incompatibility issues, not instrumentable, etc.), we took over two years to produce all these traces. We hope to save future researchers' time in producing such a set of dynamic data to enable their empirical and technical work. ================== Thanks for your interest in our dataset. Collecting this dataset took tremendous computational and human effort. Thus, please observe the following restrictions in using our dataset: - Do not redistribute this dataset without our consent. - Do not make commercial usage of this dataset. - Get a faculty, or someone in a permanent position, to agree and commit to these conditions. - When publishing your work that uses our dataset, please cite the following MSR 2021 data paper. @inproceedings{AndroidCT, title = {AndroCT: Ten Years of App Call Traces in Android}, author = {Wen Li, Xiaoqin Fu, and Haipeng Cai}, booktitle = {The 18th International Conference on Mining Software Repositories (MSR 2021), Data Showcase Track}, year = {2021}, }

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!

Android Key StatisticsAndroid OverviewAndroid Version Market ShareAndroid Vendor Market ShareAndroid vs iOS Market ShareAndroid UsersAndroid ShipmentsAndroid is the most popular operating system in...

The graph shows a comparison for app downloads worldwide from 2018 to 2024, using data from Sensor Tower and data.ai. Global app downloads have plateued in recent years, even declining, after seeing strong growth during the COVID-19 pandemic. For 2024 136 billion unique dowloads per user account were recorded. Why the difference? Source methodology explains the gap The discrepancy arises from significant differences in the methodolgy used by the sources to aggregate and generate the data. Sensor Tower reports only unique downloads per user account, excluding app updates, re-downloads, and installations on multiple devices by the same user. In contrast, data.ai includes these additional activities as well as downloads from third-party Android stores and a broader geographic scope, resulting in substantially higher total counts. As a result, Sensor Tower's numbers better reflect new user acquisition, while data.ai's encompass all market activity and total engagement. Despite stagnating downloads user spending is growing While the number of downloads is leveling off, consumer spending on in-app purchases and related revenue has grown in 2024 to 150 billion U.S. dollars, up from aroud 130 billion U.S. dollars in 2023. While gaming remains the highest grossing app category overall, the growth was driven by other categories. The entertainment, photo & video, productivity, and social networking categories ech grew by at least one billion U.S. dollars in revenue in 2024 compared to the previous year.

A large-scale dataset on the static and dynamic profiles based on function calls of 30,634 benign and malicious Android apps from eight historical years (2010 through 2017). Function calls are a commonly used means to model program behaviors, which may contribute to various code analysis approaches to assuring software correctness, reliability, and security. In particular, our dataset includes static and dynamic profiles of each app based on the same set of metrics that define the profile, enabling hybrid app analysis that reason about app behaviors from the dynamic profiles with the corresponding profiles as context. The static profiles are computed by the state-of-the-art static app analysis for Android, while the dynamic profiles are the result of running each sample app against automatically generated test inputs for ten minutes.

Introduction

Android is one of the most used mobile operating systems worldwide. Due to its technological impact, its open-source code and the possibility of installing applications from third parties without any central control, Android has recently become a malware target. Even if it includes security mechanisms, the last news about malicious activities and Android´s vulnerabilities point to the importance of continuing the development of methods and frameworks to improve its security.

To prevent malware attacks, researches and developers have proposed different security solutions, applying static analysis, dynamic analysis, and artificial intelligence. Indeed, data science has become a promising area in cybersecurity, since analytical models based on data allow for the discovery of insights that can help to predict malicious activities.

In this work, we propose to consider some network layer features as the basis for machine learning models that can successfully detect malware applications, using open datasets from the research community.

Content

This dataset is based on another dataset (DroidCollector) where you can get all the network traffic in pcap files, in our research we preprocessed the files in order to get network features that are illustrated in the next article:

López, C. C. U., Villarreal, J. S. D., Belalcazar, A. F. P., Cadavid, A. N., & Cely, J. G. D. (2018, May). Features to Detect Android Malware. In 2018 IEEE Colombian Conference on Communications and Computing (COLCOM) (pp. 1-6). IEEE.

Acknowledgements

Cao, D., Wang, S., Li, Q., Cheny, Z., Yan, Q., Peng, L., & Yang, B. (2016, August). DroidCollector: A High Performance Framework for High Quality Android Traffic Collection. In Trustcom/BigDataSE/I SPA, 2016 IEEE (pp. 1753-1758). IEEE

As of May 2025, nearly ** percent of apps in the Google Play app store were freely available. The number of free apps on the Google Play Store and the Apple Store alike has been consistently higher than the number of paid apps. By comparison, free Android apps on Amazon Appstore were roughly ** percent, while paid apps accounted for a share of ** percent of the total apps available in the store. Mobile apps and consumer spending Mobile apps have become integral to our daily routine, offering convenience and entertainment. In the second quarter of 2024, the total value of the global consumer spending on mobile apps was almost ** billion U.S. dollars, highlighting the significant role that mobile apps play in the digital economy. As of the third quarter of 2023, consumers spent an average of **** U.S. dollars on mobile apps per smartphone, which underlines the high demand for these digital solutions. App stores commission rates under scrutiny As of August 2023, the standard commission rates on revenues generated from apps hosted on the Apple App Store and the Google Play Store were set at ** percent. However, between the end of 2020 and mid-2021, both Apple and Google were forced to address the criticism of their app store policies. In 2020, the European Union drafted the Digital Market Act, with the purpose of ensuring a healthy degree of competition in the tech environment. In December 2022, Apple was reported to start planning to allow sideloading and the presence of alternative app stores on its devices. In August 2021, the United States Senate presented the Open Apps Market Act to reduce tech giants‘ control over the digital app market. As regulations are expected to promote competition in the tech and mobile environment, in March 2023, Microsoft was reported to preparing to launch a new mobile gaming store, which will compete with the Apple App Store and the Google Play Store.In 2026, mobile app spending is forecasted to reach *** billion U.S. dollars and ** billion U.S. dollars on the Apple App Store and the Google Play Store, respectively. While both Google and Apple started applying some changes in their app store policies in 2021, like lowering commission fees for small publishers generating less than *** million U.S. dollars in yearly revenues, the two tech giants might face additional restrictions and limitations in all their major markets. In the case of Apple, in 2021, the company updated its App Store policies, allowing developers to offer alternative payment methods. In 2022, Apple updated its review guidelines, requiring developers to share more information about collecting and using data, including disclosing the types of collected data and how it's used.

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.

A dataset containing 2375 samples of Android Process Memory String Dumps. The dataset is broadly composed of 2 classes: "Benign App" Memory Dumps and "Malicious App" Memory Dumps, respectively, split into 2 ZIP archives. The ZIP archives in total are approximately 17GB in size, however the unzipped contents are approximately 67GB.This dataset is derived from a subset of the APK files originally made freely available for research through the AndroZoo project [1]. The AndroZoo project collected millions of Android applications and scanned them with the VirusTotal online malware scanning service, thereby classifying most of the apps as either malicious or benign at the time of scanning. The process memory dumps in this dataset were generated through running the subset of APK files from the AndroZoo dataset in an Android Emulator, capturing the process memory of the individual process and subsequently extracting only the strings from the process memory dump. This was facilitated through building 2 applications: Coriander and AndroMemDumpBeta which facilitate the running of Apps on Android Emulators, and the capturing of process memory respectively. The source code for these software applications is available on Github. The individual samples are labelled with the SHA256 hash filename from the original AndroZoo labeling and the application package names extracted from within the specific APK manifest file. They also contain a time-stamp for when the memory dumping process took place for the specific file. The file extension used is ".dmp" to indicate that the files are memory dumps, however they only contain strings, and thus can be viewed in any simple text editor.A subset of the first 10000 APK files from the original AndroZoo dataset is also included within this dataset. The metadata of these APK files is present in the file "AndroZoo-First-10000" and the 2375 Android Apps that are the main subjects of our dataset are extracted from here..Our dataset is intended to be used in furthering our research related to Machine Learning-based Triage for Android Memory Forensics. It has been made openly available in order to foster opportunities for collaboration with other researchers, to enable validation of research results as well as to enhance the body of knowledge in related areas of research.References:[1]. K. Allix, T. F. Bissyandé, J. Klein, and Y. Le Traon. AndroZoo: Collecting Millions of Android Apps for the Research Community. Mining Software Repositories (MSR) 2016

The pendulum swung in 2022 with app downloads stagnating, after two years of solid growth under the pandemic. In 2023, some categories saw growth while others continued to stagnate, as users shifted...

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.

Protection against ransomware is particularly relevant in systems running the Android operating system, due to its huge users' base and, therefore, its potential for monetization from the attackers. In "Extinguishing Ransomware - A Hybrid Approach to Android Ransomware Detection" (see references for details), we describe a hybrid (static + dynamic) malware detection method that has extremely good accuracy (100% detection rate, with false positive below 4%).

We release a dataset related to the dynamic detection part of the aforementioned methods and containing execution traces of ransomware Android applications, in order to facilitate further research as well as to facilitate the adoption of dynamic detection in practice. The dataset contains execution traces from 666 ransomware applications taken from the Heldroid project [https://github.com/necst/heldroid] (the app repository is unavailable at the moment). Execution records were obtained by running the applications, one at a time, on the Android emulator. For each application, a maximum of 20,000 stimuli were applied with a maximum execution time of 15 minutes. For most of the applications, all the stimuli could be applied in this timeframe. In some of the traces none of the two limits is reached due to emulator hiccups. Collected features are related to the memory and CPU usage, network interaction and system calls and their monitoring is performed with a period of two seconds. The Android emulator of the Android Software Development Kit for Android 4.0 (release 20140702) was used. To guarantee that the system was always in a mint condition when a new sample is started, thus avoiding possible interference (e.g., changed settings, running processes, and modifications of the operating system files) from previously run samples, the Android operating system was each time re-initialized before running each application. The application execution process was automated by means of a shell script that made use of Android Debug Bridge (adb) and that was run on a Linux PC. The Monkey application exerciser was used in the script as a generator of the aforementioned stimuli. The Monkey is a command-line tool that can be run on any emulator instance or on a device; it sends a pseudo-random stream of user events (stimuli) into the system, which acts as a stress test on the application software.

In this dataset, we provide both per-app CSV files as well as unified files, in which CSV files of single applications have been concatenated. The CSV files contain the features extracted from the raw execution record. The provided files are listed below:

• ransom-per_app-csv.zip - features obtained by executing ransomware applications, one CSV per application

• ransom-unified-csv.zip - features obtained by executing ransomware applications, only one CSV file

App Revenue Key StatisticsMobile Ad SpendApp and Game RevenuesiOS App and Game RevenueGoogle Play App and Game RevenueGaming App RevenuesiOS Gaming App RevenueGoogle Play Gaming App RevenueApp...

Key Google Play StatisticsGoogle Play App and Game RevenueGoogle Play Gaming App RevenueGoogle Play App RevenueGoogle Play App and Game DownloadsGoogle Play Game DownloadsGoogle Play App...

A dataset consisting of 751,500 English app reviews of 12 online shopping apps. The dataset was scraped from the internet using a python script. This ShoppingAppReviews dataset contains app reviews of the 12 most popular online shopping android apps: Alibaba, Aliexpress, Amazon, Daraz, eBay, Flipcart, Lazada, Meesho, Myntra, Shein, Snapdeal and Walmart. Each review entry contains many metadata like review score, thumbsupcount, review posting time, reply content etc. The dataset is organized in a zip file, under which there are 12 json files and 12 csv files for 12 online shopping apps. This dataset can be used to obtain valuable information about customers' feedback regarding their user experience of these financially important apps.

According to a survey of mobile smartphone users conducted in December 2022 in the United States, over six in ** respondents were likely or highly likely to opt out of tracking when Google introduced new features allowing Android users to do so. Among these, ** percent of users aged 55 and over reported being highly likely to limit the app tracking activity from companies and advertisers on their Android devices, and ** percent of users aged between 18 and 34 reported the same.

To research the illegal activities of underground apps on Telegram, we have created a dataset called TUApps. TUApps is a progressively growing dataset of underground apps, collected from September 2023 to February 2024, consisting of a total of 1,000 underground apps and 200 million messages distributed across 71,332 Telegram channels.
In the process of creating this dataset, we followed strict ethical standards to ensure the lawful use of the data and the protection of user privacy. The dataset includes the following files:
(1) dataset.zip: We have packaged the underground app samples. The naming of Android app files is based on the SHA256 hash of the file, and the naming of iOS app files is based on the SHA256 hash of the publishing webpage.
(2) code.zip: We have packaged the code used for crawling data from Telegram and for performing data analysis.
(3) message.zip: We have packaged the messages crawled from Telegram, the files are named after the names of the channels in Telegram.
Availability of code and messages
Upon acceptance of our research paper, the dataset containing user messages and the code used for data collection and analysis will only be made available upon request to researchers who agree to adhere to strict ethical principles and maintain the confidentiality of the data.

The size of the Mobile Application Market was valued at USD 285.96 Billion in 2023 and is projected to reach USD 698.16 Billion by 2032, with an expected CAGR of 13.60% during the forecast period. The mobile application market has experienced remarkable growth over the past decade, driven by rapid technological advancements and the widespread adoption of smartphones. This market encompasses a diverse range of apps across categories such as gaming, productivity, health, entertainment, and e-commerce, catering to both individual and business needs. The proliferation of app stores like Google Play and the Apple App Store has made it easier for developers to reach a global audience, while increasing internet penetration and affordable mobile devices have expanded user bases. Emerging technologies, such as artificial intelligence, augmented reality, and 5G connectivity, are reshaping app functionalities, offering enhanced user experiences and real-time interactions. Additionally, the growing preference for subscription-based models and in-app purchases has significantly contributed to revenue generation. As mobile apps continue to play a crucial role in various industries, businesses are leveraging them to improve customer engagement and streamline operations. With the rising demand for innovative and customized applications, the mobile app market is set to remain a dynamic and competitive space, presenting significant opportunities for developers and enterprises worldwide.Mobile Application Market Concentration & CharacteristicsThe mobile application market is highly concentrated, with a few major players dominating the market share. These players include Apple, Google, Amazon, Microsoft, and Samsung. The market is characterized by innovation and rapid technological advancements, with new technologies and features being introduced on a regular basis. Regulations and product substitutes also play a significant role in shaping the market landscape. End-user concentration is high in certain segments, such as banking and retail, where mobile applications have become essential for day-to-day operations.Key Mobile Application Market Trends HighlightedIncreased adoption of mobile devices and the proliferation of mobile internet connectivity.Rising demand for mobile applications across various industries, including banking, retail, and healthcare.Growing popularity of mobile gaming and the rise of esports.Advancements in mobile technology, such as augmented reality (AR) and virtual reality (VR).Increased focus on app security and data privacy.Key Region or Country & Segment to Dominate the MarketRegion: North America is the largest market for mobile applications due to high smartphone penetration and a large user base.Country: China is a major market for mobile applications, with a large number of smartphone users and a growing app economy.Segment: Gaming is the largest segment of the mobile application market, driven by the popularity of mobile games such as Candy Crush and Pokémon Go.Mobile Application Market Product InsightsCategories:Gaming: Mobile games are highly popular and generate significant revenue.Non-Gaming: Non-gaming applications include productivity, social media, and communication tools.Platform:iOS: Apple's iOS platform is known for its user-friendly interface and high-quality apps.Android: Google's Android platform is open source and offers a wide range of apps.Windows: Microsoft's Windows platform is primarily used on tablets and laptops.End-User:Banking: Mobile banking applications provide convenience and security for customers.Retail: Mobile retail applications offer online shopping and loyalty programs.Airlines: Mobile airline applications allow users to book flights, check-in, and receive updates.Media: Mobile media applications include news, entertainment, and streaming services.Education: Mobile education applications provide educational resources and online learning opportunities.Transport: Mobile transport applications assist users with navigation, ride-sharing, and public transportation.Hotels & Restaurants: Mobile hotel and restaurant applications offer booking, loyalty programs, and food delivery services.Government: Mobile government applications provide citizen services and access to information.Report Coverage & DeliverablesThis comprehensive mobile application market research report covers the following market segmentations:Categories: Gaming and Non-GamingPlatform: iOS, Android, Windows, and OthersEnd-User: Banking, Retail, Airlines, Media, Education, Transport, Hotels & Restaurants, GovernmentDriving Forces: What's Propelling the Mobile Application MarketRising smartphone penetrationTechnological advancementsIncreasing demand for convenience and efficiencyGrowing popularity of mobile gamingExpansion of mobile payment systemsChallenges and Restraints in Mobile Application MarketApp security and data privacy concernsLack of skilled app developersCompetition from web-based applicationsLimited monetization options for non-gaming appsEmerging Trends in Mobile Application MarketAugmented reality (AR) and virtual reality (VR)Artificial intelligence (AI)Blockchain technologyMobile health (mHealth)Wearable devicesGrowth Catalysts in Mobile Application IndustryGovernment initiatives to promote mobile app developmentInvestments in mobile infrastructure and connectivityPartnership between app developers and device manufacturersKey Companies in the Mobile Application Market IncludeAppleGoogleAmazonMicrosoftSamsungAppinventivHyperlink InfoSystemDesignliMercury DevelopmentWonderment AppsWebClues InfotechNaked DevelopmentApptunixTheAppLabbEchoinnovate ITPrismetricTrango TechLight IT GlobalApp MaistersNMG TechnologiesRecent Developments in Mobile ApplicationAcquisition of Incapptic Connect GmbH by Mobileiron Inc.Acquisition of AppSheet by Google LLCGrowing investments in mobile game developmentRise of mobile e-commerce applicationsIncreasing use of mobile applications in healthcare and education Recent developments include: In May 2020, a mobile app development company, Incapptic Connect GmbH has been acquired by Mobilelron Inc. It will help in deploying and developing a secure base for application development. In January 2020, a provider of a no-code development platform, AppSheet had been acquired by Google LLC. It will help in developing software for applications. Various companies like EA Sports, Ubisoft, and Gameloft are investing huge sums for the development of high graphic games that will be playable on smartphones as well. The development in the mobile application market has resulted in the growing usage of applications in various sectors like the banking sector, government sector, retail sector, hotel & restaurant sector, education sector, and airlines sector. The largest market share in the global mobile application in the global market is held by the North American region owing to the presence of a large number of mobile users. , This global mobile application market research report contains factors that drive the growth of the mobile application market in the global market along with the factors that restrict its growth in the global market. The opportunities available for the growth of the mobile application market during the forecasted period are mentioned. The impact of COVID 19 on the sales revenue of the mobile application market all across the globe is mentioned. The future growth during the forecasted period is estimated and mentioned.. Key drivers for this market are: App security and data privacy concerns Lack of skilled app developers Competition from web-based applications Limited monetization options for non-gaming apps. Potential restraints include: App security and data privacy concerns Lack of skilled app developers Competition from web-based applications Limited monetization options for non-gaming apps. Notable trends are: Augmented reality (AR) and virtual reality (VR) Artificial intelligence (AI) Blockchain technology Mobile health (mHealth) Wearable devices.