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.

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.

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.

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.

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.

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.

At MFour, our Behavioral Data stands out for its uniqueness and depth of insights. What makes our data genuinely exceptional is the combination of several key factors:

• First-Party Opt-In Data: Our data is sourced directly from our opt-in panel of consumers who willingly participate in research and provide observed behaviors. This ensures the highest data quality and eliminates privacy concerns. CCPA compliant.

• Unparalleled Data Coverage: With access to 3B+ billion events, we have an extensive pool of participants who allow us to observe their brick + mortar location visitation, app + web smartphone usage, or both. This large-scale coverage provides robust and reliable insights.

• Our data is generally sourced through our Surveys On The Go (SOTG) mobile research app, where consumers are incentivized with cash rewards to participate in surveys and share their observed behaviors. This incentivized approach ensures a willing and engaged panel, leading to the highest-quality data.

The primary use cases and verticals of our Behavioral Data Product are diverse and varied. Some key applications include:

• Data Acquisition and Modeling: Our data helps businesses acquire valuable insights into consumer behavior and enables modeling for various research objectives.

• Shopper Data Analysis: By understanding purchase behavior and patterns, businesses can optimize their strategies, improve targeting, and enhance customer experiences.

• Media Consumption Insights: Our data provides a deep understanding of viewer behavior and patterns across popular platforms like YouTube, Amazon Prime, Netflix, and Disney+, enabling effective media planning and content optimization.

• App Performance Optimization: Analyzing app behavior allows businesses to monitor usage patterns, track key performance indicators (KPIs), and optimize app experiences to drive user engagement and retention.

• Location-Based Targeting: With our detailed location data, businesses can map out consumer visits to physical venues and combine them with web and app behavior to create predictive ad targeting strategies.

• Audience Creation for Ad Placement: Our data enables the creation of highly targeted audiences for ad campaigns, ensuring better reach and engagement with relevant consumer segments.

The Behavioral Data Product complements our comprehensive suite of data solutions in the broader context of our data offering. It provides granular and event-level insights into consumer behaviors, which can be combined with other data sets such as survey responses, demographics, or custom profiling questions to offer a holistic understanding of consumer preferences, motivations, and actions.

MFour's Behavioral Data empowers businesses with unparalleled consumer insights, allowing them to make data-driven decisions, uncover new opportunities, and stay ahead in today's dynamic market landscape.

The main objective of this research is to classify the activities developers perform in real projects and map them to the phases of the DevOps cycle.

We analyzed commit messages and changes made during the development of Android applications within a dataset of 7,441 Android repositories, with 1,983,967 total commits. First, we performed a qualitative manual analysis of 2,000 sampled revisions to identify the activities of Android mobile application developers and correlate them with DevOps practices. The obtained data were then used to train an ML algorithm and extend the classification to the whole dataset. In addition, metadata extracted from the repositories was used to refine the results.

Finally, we categorized mobile development activities based on DevOps practices to understand how those practices are applied and potential open directions for practitioners and researchers.

This is the dataset for the paper: Exploring Accessibility Trends and Challenges in Mobile App Development: A Study of Stack Overflow Questions

This paper was accepted for publication at the 58th Hawaii International Conference on System Sciences (HICSS) - Software Technology Track

Preprint: https://arxiv.org/abs/2409.07945

Find IT professionals across the globe with Success.ai’s App Developer Data and B2B Contact Data. Includes verified work emails, phone numbers, and continuously updated datasets. Perfect for outreach and marketing. Best price guaranteed.

The amount of Android apps available for download is constantly increasing, exerting a continuous pressure on developers to publish outstanding apps. Google Play (GP) is the default distribution channel for Android apps, which provides mobile app users with metrics to identify and report apps quality such as rating, amount of downloads, previous users comments, etc. In addition to those metrics, GP presents a set of top charts that highlight the outstanding apps in different categories. Both metrics and top app charts help developers to identify whether their development decisions are well valued by the community. Therefore, app presence in these top charts is a valuable information when understanding the features of top-apps. In this paper we present Hall-of-Apps, a dataset containing top charts' apps metadata extracted (weekly) from GP, for 4 different countries, during 30 weeks. The data is presented as (i) raw HTML files, (ii) a MongoDB database with all the information contained in app's HTML files (e.g., app description, category, general rating, etc.), and (iii) data visualizations built with the D3.js framework. A first characterization of the data along with the urls to retrieve it can be found in our online appendix: https://thesoftwaredesignlab.github.io/hall-of-apps-tools/

A complete assessment of all urology apps, including its description and information about its creators.

Dataset of smartphone-based finger tapping test submitted to Scientific Data journal.

Recent developments include: , May 2023: IBM launched IBM Hybrid Cloud Mesh, a SaaS solution intended to help businesses manage their hybrid multi-cloud architecture. With "Application-Centric Connectivity" at its core, IBM Hybrid Cloud Mesh is designed to automate the process, management, and observability of application connectivity in and between public and private clouds, assisting modern enterprises in managing their infrastructure across hybrid multi-cloud and heterogeneous environments., June 2022: Oracle has introduced the X10M, the most recent generation of Oracle Exadata platforms, which offers unmatched performance and availability for all Oracle Database workloads. These systems start at the same price as the previous generation, have more capacity, and support database consolidation to higher levels, but they also offer a much superior overall package., Enterprise Mobile Application Development Platform Market Segmentation, Enterprise Mobile Application Development Platform Deployment Outlook. Key drivers for this market are: Increasing mobile device penetration Need for improved employee productivity Enhanced customer engagement Digital transformation initiatives Growing use of IoT and wearable devices. Potential restraints include: Data security concerns High development costs Device fragmentation Limited customization options. Notable trends are: Data security concerns High development costs Device fragmentation Limited customization options.

This is the dataset for the paper: A Developer-Centric Study Exploring Mobile Application Security Practices and Challenges

This paper was accepted for publication at the International Conference on Software Maintenance and Evolution (ICSME 2024) - Industry Track

Preprint: https://arxiv.org/abs/2408.09032

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
      ]
    ]
  }
}

A replication package including a manually analysed dataset of a random sample of 1,200 app reviews and 1,200 issue comments from 12 diverse projects that exist on both Google App Store and GitHub, the results of our machine learning and deep learning approaches, our survey questions and raw responses from app developers to the survey.

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The presence of business in the digital space is a must now. Indeed, there’s hardly any company, be it a small startup or an international corporation, that wouldn’t be available online. For this, the company may use one of two options — to develop an app or a website, or both.

In the case of a limited budget, business owners often have to make a choice. Thus, considering that mobile traffic bypassed the desktop’s in 2016 and continues to grow, it becomes obvious that the business should become accessible and convenient for smartphone users. But what is better a responsive website or a mobile application?

Entrepreneurs often turn to development companies to ask this question. Lacking sufficient knowledge, they hope to get answers to their questions from people with experience in this field. So, we decided to compile a guide that will give you clear and understandable information.

Mobile app

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Let's look at the stats. It will help you understand why a mobile app may be the obvious choice for your client.

In 2019, smartphone users installed about 204 billion(!) applications on their devices. On average, this is more than 26 applications per inhabitant of the planet Earth. And if this is not enough evidence, here’s one more point. The expected revenue of mobile applications will be $189 billion in 2020.

It sounds impressive, but this does not mean that a mobile application is something indispensable for every business. Not at all. Let's go through the pros and cons of a mobile application and try to understand when it is needed.

Pros

• A new level of interaction. Mobile applications are a more convenient method of interaction. They load and process content faster. One more useful feature is notifications. Perhaps, applications are the best way to inform users about new updates, promotions, and other news (who will read long letters in the mail?).
• Personalized targeting. Mobile applications are ideal for products or services that need to be used on an ongoing basis. The options like creating accounts, entering profile information, etc., make applications more personalized than websites. All this allows the business to target their audience more accurately without wasting money.
• Offline usage. That’s another major advantage. Applications can provide users with access to content without an internet connection.

Cons

• Development costs. In order to reach the maximum audience with a mobile app, it is necessary to cover two main operating systems — iOS and Android. Development for each OS can be too expensive for small business owners and they will have to make difficult choices. The way out of this situation is cross-platform development. Why? Because there’s no need to guess which platform targets prefer using — iOS or Android. Instead, you create just one app that runs seamlessly on both platforms.

• Maintenance. The application is a technical product that needs constant support. Upgrades should be carried out in a timely manner. Often, users need to personally update applications by downloading a new version, which is annoying. Regular bug-fixing for various devices (smartphones, tablets) and different operating systems might be a real problem. Plus, any update should be confirmed by the store where the application is placed.

• Suitable for businesses that provide interactive and personalized content (refers to all lifestyle and healthcare solutions), require regular app usage (for instance, to-do lists), rely on visual interaction and so on. For games, like Angry Birds, creating an app is also a wise choice.

Website

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In order to be convenient for users of mobile devices, a website should be responsive. We want to make an emphasis on this since it is critically important. Most of the traffic on the Internet comes from mobile devices, so your website should be adaptable, or in other words, mobile-friendly. If a mobile user needs to zoom in all the necessary elements and text to see something, they will immediately quit your website.

On the other hand, a responsive website has the following benefits.

Pros

• Maintenance. Maintaining a website is less costly. When compared to applications where the user mu...

Gemma Catolino - Doctorial Symposium ICSE 2018

The global low code development market is approximated at a value of US$ 22.5 billion in 2024 and is calculated to increase at a CAGR of 26.8% to reach US$ 241.9 billion by the end of 2034.

Report Attribute	Detail
Low Code Development Market Size (2024E)	US$ 22.5 Billion
Forecasted Market Value (2034F)	US$ 241.9 Billion
Global Market Growth Rate (2024 to 2034)	26.8% CAGR
South Korea Market Value (2034F)	US$ 13.1 Billion
On-premise Demand Growth Rate (2024 to 2034)	24.9% CAGR
Key Companies Profiled	Mendix Technology BV; Zoho Corporation Pvt. Ltd.; Kintonne; Appian Corporation; Microsoft Corporation; Salesforce.com, Inc.; NewGen; AuraQuantic; Oracle Corporation; Pegasystems Inc.; ServiceNow Inc.; Creatio; Quick Base; Betty Blocks; TrackVia; OutSystems Inc.

Country-wise Analysis

Attribute	United States
Market Value (2024E)	US$ 2.5 Billion
Growth Rate (2024 to 2034)	26.7% CAGR
Projected Value (2034F)	US$ 26.7 Billion

Attribute	China
Market Value (2024E)	US$ 2.5 Billion
Growth Rate (2024 to 2034)	26.7% CAGR
Projected Value (2034F)	US$ 27 Billion

Category-wise Analysis

Attribute	BFSI
Segment Value (2024E)	US$ 4.5 Billion
Growth Rate (2024 to 2034)	27.8% CAGR
Projected Value (2034F)	US$ 52.2 Billion

Attribute	Cloud-based Low Code Development Platforms
Segment Value (2024E)	US$ 14.6 Billion
Growth Rate (2024 to 2034)	27.7% CAGR
Projected Value (2034F)	US$ 169.3 Billion