What people complain about drone apps? a large-scale empirical study of Google play store reviews

Within the past few years, there has been a tremendous increase in the number of UAVs (Unmanned Aerial Vehicle) or drones manufacture and purchase. It is expected to proliferate further, penetrating into every stream of life, thus making its usage inevitable. The UAV’s major components are its physical hardware and programming software, which controls its navigation or performs various tasks based on the field of concern. The drone manufacturers launch the controlling app for the drones in mobile app stores. A few drone manufacturers also release development kits to aid drone enthusiasts in developing customized or more creative apps. Thus, the app stores are also expected to be flooded with drone-related apps in the near future. With various active research and studies being carried out in UAV’s hardware field, no effort is dedicated to studying/researching the software side of UAV. Towards this end, a large-scale empirical study of UAV or drone-related apps of the Google Play Store Platform is conducted. The study consisted of 1,825 UAV mobile apps, across twenty-five categories, with 162,250 reviews. Some of the notable findings of the thesis are (a) There are 27 major types of issues the drone app users complain about, (b) The top four complaints observed are Functional Error (27.9%), Device Compatibility (16.8%), Cost (16.2%) and Connection/Sync (15.6%), (c) The top four issues for which the UAV manufactures or Drone app developers provide feedback to user complaints are Functional Error (40.9%), Cost (33.3%), Device Compatibility (23.1%) and ConnectionSync (16%), (d) Developers respond to the most frequently occurring complaints rather than the most negatively impacting ones

How Should You plan Your App’s Features? Selecting and Prioritizing A Mobile App’s Initial Features Based on User Reviews

The app market is extremely competitive, with users typically having several alternative app possibilities. To attract and retain users, it is imperative for developers to consider the ratings and reviews their apps receive. App reviews frequently contain feature requests, sometimes hidden among complaints. Developers use these complaints and requests to improve their apps, thus increasing their rating which is incredibly important for attracting new users. Unfortunately, developers of new apps are at a severe disadvantage: They do not have the benefit of existing reviews, with only the reviews of similar apps to potentially rely upon. To address this problem, we conducted a study and developed a novel technique that extracts feature requests from similar, existing apps to help prioritize the features and requirements important in an initial app release. We compared different classification models in order to identify most appropriate classifier for classifying reviews category-wise. We found that there is not one single classifier that could have a higher accuracy than others for all categories.Our study also involved extracting features from user reviews in the Google Play store. The features were presented to 17 Android developers twice; once without applying our technique and once after applying our technique. Our proposed technique created a 48\% reduction in the number of features considered high priority by participants; helping developers focus on what features to consider for their apps. We surprisingly found that the frequency of requested features did not impact the developer\u27s decisions in prioritizing the features in the inclusion of new apps

Mobile App Installation: the Role of Precautions and Desensitization

The purpose of this research is to investigate precautions that consumers take before installing mobile apps and consumer’s potential desensitization to excessive app permission requests. Through a survey of 209 participants, a prediction model was created that attempts to predict whether respondents would download applications asking for excessive permissions. The model results indicate those that take more precautions are less likely to download apps requesting excessive permissions. However, the precautions taken by participants may be inadequate and may leave consumers with a false since of security. Another key finding with the support of Communication Theory and the C-HIP Model is that some consumers have become desensitized to excessive permission requests. These consumers knowingly install apps requesting excessive permissions for reasons such as nothing bad has happened to them before, they trust the market, or they really want the app. The security implications of permission desensitization and inadequate precautions are discussed

Implementing Ethics for a Mobile App Deployment

This paper discusses the ethical dimensions of a research project in which we deployed a personal tracking app on the Apple App Store and collected data from users with whom we had little or no direct contact. We describe the in-app functionality we created for supporting consent and withdrawal, our approach to privacy, our navigation of a formal ethical review, and navigation of the Apple approval process. We highlight two key issues for deployment-based research. Firstly, that it involves addressing multiple, sometimes conflicting ethical principles and guidelines. Secondly, that research ethics are not readily separable from design, but the two are enmeshed. As such, we argue that in-action and situational perspectives on research ethics are relevant to deployment-based research, even where the technology is relatively mundane. We also argue that it is desirable to produce and share relevant design knowledge and embed in-action and situational approaches in design activities

A survey of app store analysis for software engineering

App Store Analysis studies information about applications obtained from app stores. App stores provide a wealth of information derived from users that would not exist had the applications been distributed via previous software deployment methods. App Store Analysis combines this non-technical information with technical information to learn trends and behaviours within these forms of software repositories. Findings from App Store Analysis have a direct and actionable impact on the software teams that develop software for app stores, and have led to techniques for requirements engineering, release planning, software design, security and testing. This survey describes and compares the areas of research that have been explored thus far, drawing out common aspects, trends and directions future research should take to address open problems and challenges

Assessing the Impact of Usability from Evaluating Mobile Health Applications

Software applications that are used to monitor, track, and improve health are called Mobile Health Applications or mHAs. They are developed with or without the help of medical professionals to potentially aid health, achieve health goals and improve lifestyle or behavior. Although mobile Health Applications have been on the market since the advent of mobile applications, the pandemic saw to a 25% increase in the number of mobile health applications available on the app stores. This indicates the growing demand for mHAs. This research was conducted to evaluate the impact of usability of mobile health applications. The dataset used to carry out this research is a review data set of health-condition management focused apps. These apps managed conditions like Diabetes, Depression, Hypertension, etc. System Usability Score, Net Promoter Score, App Ratings, Patient engagement was some of the features that were used to conduct the research. There were low correlations between App’s reaction to dangerous information and usability score (0.17), Existence of privacy policy and usability score (-0.032), IOS App Rating and Usability Score (0.053), Android App Rating and Usability Score (-0.029). Patient, Caregiver/Clinicians engagement-based variables like ‘does the app makes reference to specific disease guidelines’, ‘in what way does the app engage patients’, ‘does the app provide support through social media’ showed higher correlations with usability scores and clinical utility. It is recommended that to evaluate the usability of mobile health applications, a combination of usability measuring methods be used

Mobile App Monetization – Expectations and Attitudes Formed by Users in Response to Advertising and Pay To Download Monetization Models

Professional project report submitted in partial fulfillment of the requirements for the degree of Masters of Arts in Journalism from the School of Journalism, University of Missouri--Columbia.This research attempts to discover the attitudes and expectations formed by different methods of mobile app monetization in users who have paid to download apps or encountered any method of monetization in their use of apps. The goal is to help lead app developers and marketers to proper conclusions about how to monetize mobile apps in ways that are unobtrusive to users. The research is completed via focus group and online survey. Upon examination, it is clear users prefer a single method of monetization -- pay to download OR advertising based, for example -- rather than a hybrid approach commonly seen in traditional media. Men may be more likely to download apps on a more frequent basis, also paying for upgrades more frequently. Users seek out brands they recognize and rely on friends and other users to provide them information about apps they should download.Includes bibliographic references

Mining app reviews to support software engineering

The thesis studies how mining app reviews can support software engineering. App reviews —short user reviews of an app in app stores— provide a potentially rich source of information to help software development teams maintain and evolve their products. Exploiting this information is however difficult due to the large number of reviews and the difficulty in extracting useful actionable information from short informal texts. A variety of app review mining techniques have been proposed to classify reviews and to extract information such as feature requests, bug descriptions, and user sentiments but the usefulness of these techniques in practice is still unknown. Research in this area has grown rapidly, resulting in a large number of scientific publications (at least 182 between 2010 and 2020) but nearly no independent evaluation and description of how diverse techniques fit together to support specific software engineering tasks have been performed so far. The thesis presents a series of contributions to address these limitations. We first report the findings of a systematic literature review in app review mining exposing the breadth and limitations of research in this area. Using findings from the literature review, we then present a reference model that relates features of app review mining tools to specific software engineering tasks supporting requirements engineering, software maintenance and evolution. We then present two additional contributions extending previous evaluations of app review mining techniques. We present a novel independent evaluation of opinion mining techniques using an annotated dataset created for our experiment. Our evaluation finds lower effectiveness than initially reported by the techniques authors. A final part of the thesis, evaluates approaches in searching for app reviews pertinent to a particular feature. The findings show a general purpose search technique is more effective than the state-of-the-art purpose-built app review mining techniques; and suggest their usefulness for requirements elicitation. Overall, the thesis contributes to improving the empirical evaluation of app review mining techniques and their application in software engineering practice. Researchers and developers of future app mining tools will benefit from the novel reference model, detailed experiments designs, and publicly available datasets presented in the thesis