Application of Developers' and Users' Dependent Factors in App Store Optimization

This paper presents an application of developers' and users' dependent factors in the app store optimization. The application is based on two main fields: developers' dependent factors and users' dependent factors. Developers' dependent factors are identified as: developer name, app name, subtitle, genre, short description, long description, content rating, system requirements, page url, last update, what's new and price. Users' dependent factors are identified as: download volume, average rating, rating volume and reviews. The proposed application in its final form is modelled after mining sample data from two leading app stores: Google Play and Apple App Store. Results from analyzing collected data show that developer dependent elements can be better optimized. Names and descriptions of mobile apps are not fully utilized. In Google Play there is one significant correlation between download volume and number of reviews, whereas in App Store there is no significant correlation between factors

Online reviews as first class artifacts in mobile app development.

This paper introduces a framework for developing mobile apps. The framework relies heavily on app stores and, particularly, on online reviews from app users. The underlying idea is that app stores are proxies for users because they contain direct feedback from them. Such feedback includes feature requests and bug reports, which facilitate design and testing respectively. The framework is supported by MARA, a prototype system designed to automatically extract relevant information from online reviews

Knowledge Graph Development for App Store Data Modeling

Usage of mobile applications has become a part of our lives today, since every day we use our smartphones for communication, entertainment, business and education. High demand on apps has led to significant growth of supply, yet large offer has caused complications in users’ search of the one suitable application. The authors have made an attempt to solve the problem of facilitating the search in app stores. With the help of a website crawling software a sample of data was retrieved from one of the well-known mobile app stores and divided into 11 groups by types. These groups of data were used to construct a Knowledge Schema – a graphic model of interconnections of data that characterize any mobile app in the selected store. Schema creation is the first step in the process of developing a Knowledge Graph that will perform applications clustering to facilitate users’ search in app stores

Users’ Sentiment Analysis toward National Digital Library of India: a Quantitative Approach for Understanding User perception

Sentiment analysis is also known as opinion mining. Sentiment analysis is contextual mining of text which identifies and extracts subjective information in textual data. It is extremely used by business, educational organizations, and social media monitoring to gain the general outlook of the wide public regarding their product and policy. The current study looks for gaining insights into user reviews on the National Digital Library of India (NDLI) mobile app (android and iOS). For this purpose, sentiment analysis will be used. It yields an average of 3.64/5 ratings based on 11,861 reviews. The dataset includes a total of 4560 user reviews in which iOS and the android app have received 33 and 4527 reviews respectively as on 7th Sept 2021. AppBot and AppFollow analytics software is used to extract and collect user review information as raw data. The study shows the reviews of the NDLI mobile app as 2130 positive and 1808 negative sentiments for android & 6 positive and 22 negative sentiments for iOS. The overall sentiment score is found to be 66%. The results of the sentiment analysis show that Android users are more satisfied as compared to iOS users. The most frequent complaints made by the users are functional errors, feature requests and app crashes. Some of the major issues that users have complained about are books that need to be downloaded before reading and some pdfs are blank once opened. The value of this research is getting an insight into the behaviour of users towards using apps on different platforms (Android vs iOS) and provides valuable results for the app developers in monitoring usage and enhancing features for the satisfaction of users. The findings reveal that stakeholders/developers need to pay more attention to make the app more user-friendly

Driving the Technology Value Stream by Analyzing App Reviews

An emerging feature of mobile application software is the need to quickly produce new versions to solve problems that emerged in previous versions. This helps adapt to changing user needs and preferences. In a continuous software development process, the user reviews collected by the apps themselves can play a crucial role to detect which components need to be reworked. This paper proposes a novel framework that enables software companies to drive their technology value stream based on the feedback (or reviews) provided by the end-users of an application. The proposed end-to-end framework exploits different Natural Language Processing (NLP) tasks to best understand the needs and goals of the end users. We also provide a thorough and in-depth analysis of the framework, the performance of each of the modules, and the overall contribution in driving the technology value stream. An analysis of reviews with sixteen popular Android Play Store applications from various genres over a long period of time provides encouraging evidence of the effectiveness of the proposed approach

Do users care about ad's performance costs? Exploring the effects of the performance costs of in-app ads on user experience

Context: In-app advertising is the primary source of revenue for many mobile apps. The cost of advertising (ad cost) is non-negligible for app developers to ensure a good user experience and continuous profits. Previous studies mainly focus on addressing the hidden performance costs generated by ads, including consumption of memory, CPU, data traffic, and battery. However, there is no research on analyzing users’ perceptions of ads’ performance costs to our knowledge. / Objective: To fill this gap and better understand the effects of performance costs of in-app ads on user experience, we conduct a study on analyzing user concerns about ads’ performance costs. / Method: First, we propose RankMiner, an approach to quantify user concerns about specific app issues, including performance costs. Then, based on the usage traces of 20 subject apps, we measure the performance costs of ads. Finally, we conduct correlation analysis on the performance costs and quantified user concerns to explore whether users complain more for higher performance costs. / Results: Our findings include the following: (1) RankMiner can quantify users’ concerns better than baselines by an improvement of 214% and 2.5% in terms of Pearson correlation coefficient (a metric for computing correlations between two variables) and NDCG score (a metric for computing accuracy in prioritizing issues), respectively. (2) The performance costs of the with-ads versions are statistically significantly larger than those of no-ads versions with negligible effect size; (3) Users are more concerned about the battery costs of ads, and tend to be insensitive to ads’ data traffic costs. / Conclusion: Our study is complementary to previous work on in-app ads, and can encourage developers to pay more attention to alleviating the most user-concerned performance costs, such as battery cost