What Makes a Great Mobile App? A Quantitative Study Using a New Mobile Crawler

Jiang, Zexun; Kuang, Ruifeng; Gong, Jianya; Yin, Hao; Lyu, Yeoung-Su; Zhang, Xu

doi:10.1109/sose.2018.00037

Cited by 14 publications

(3 citation statements)

References 12 publications

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“…This finding suggests that content optimization in access networks could be significant, with local app content being placed on servers closer to end-users. Jiang et al [202] looked into the relationship between app popularity and network performance. They discovered that over 60% of popular apps optimize network delay under 350ms, highlighting the importance of app service network performance.…”

Section: B App Traffic Patternsmentioning

confidence: 99%

Smartphone App Usage Analysis: Datasets, Methods, and Applications

Tong

Wang

et al. 2022

IEEE Commun. Surv. Tutorials

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As smartphones have become indispensable personal devices, the number of smartphone users has increased dramatically over the last decade. These personal devices, which are supported by a variety of smartphone apps, allow people to access Internet services in a convenient and ubiquitous manner. App developers and service providers can collect fine-grained app usage traces, revealing connections between users, apps, and smartphones. We present a comprehensive review of the most recent research on smartphone app usage analysis in this survey. Our survey summarizes advanced technologies and key patterns in smartphone app usage behaviors, all of which have significant implications for all relevant stakeholders, including academia and industry. We begin by describing four data collection methods: surveys, monitoring apps, network operators, and app stores, as well as nine publicly available app usage datasets. We then systematically summarize the related studies of app usage analysis in three domains: app domain, user domain, and smartphone domain. We make a detailed taxonomy of the problem studied, the datasets used, the methods used, and the significant results obtained in each domain. Finally, we discuss future directions in this exciting field by highlighting research challenges.

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Section: B App Traffic Patternsmentioning

confidence: 99%

Smartphone App Usage Analysis: Datasets, Methods, and Applications

Tong

Wang

et al. 2022

IEEE Commun. Surv. Tutorials

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“…Here, the analysis was limited by the nature of data collected and view of the individuals on the selected parameters. Jiang et al (2018) utilized a new mobile application crawler to gather information, and they conducted a quantitative analysis on the collected data to study the average latency of mobile applications. Alderson et al (2005) proposed a qualitative approach to model a real-time internet traffic, where they analyzed the degree of connectivity against the bandwidth at a router level.…”

Section: Related Workmentioning

confidence: 99%

Quantification label for mobile apps

Habib

Marimuthu

2021

JER is an international, peer-reviewed journal that publishes f

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Nowadays every product we purchase has a tag informing its users how much an automobile or an appliance will cost to run and how much energy a food product will provide on consumption, for example. With the advent of Internet, purchasing Apps becomes habitual, as Apps facilitate easy way of trading and generate money according to App online stores. Currently, Apps provide label that contains a combination of developer info, memory size, category, operating system compatibility, user ratings and reviews, and privacy policy. However, the product label information is asymmetrical and it varies with the developer. It is challenging to develop common standards explicit to users, as their needs are dynamic; moreover, the App undergoes a series of state changes during execution, according to the usage pattern. We propose a quantification label framework to assess the mobile/web App using a set of quantifiers, such as functionality, degree and domain of connectivity, battery consumption, and its vulnerability index for generating a set of standard labels. The corresponding labels in the label frame are: features, popularity, energy consumption and security. Further, we utilized principal component analysis (PCA), a statistical technique to assist the customers in selecting the prominent features from the dataset and to compare related apps, which analyzes the quantifiers in the given datasets and distinguish them according to the variances. The experimental results in quantifying a real time traffic data of an internet based App validated the proposed label by revealing the key features to the users; moreover, PCA carried out on four sets of traffic data enabled the selection of prominent data, and the results also revealed the possible embedding of the framework within an App for dynamic monitoring.

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“…Cell phones can be used anywhere unlike to the desktops and laptops in easier manner. Due to these reasons, mobile applications are specified with some limitations in different extensions impacting the working qualities of an application created with a way to deal with work on various platforms [3] [4]. Down to the perspective, mobile phones were built in a manner to have obliged limit, screen size, low preparing capacities, less intelligent and poor accessibility to the system.…”

Section: Introductionmentioning

confidence: 99%

Mobile Phone Maintainability Prediction using MCDM Methodology

2019

IJRTE

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For software engineering it is essential to build a software that is qualitative from every perspective. As the interest of the clients in mobile applications getting higher and higher day by day, the quality turns out to be a major concern. Ample number of researches shows issues with the maintenance of the applications once they are public. Different studies showed various evaluation methods for estimating maintainability but only few were defined for mobile applications. Also, none of the approaches propounded gives a specified way for evaluating maintainability of these portable applications. The present research work, examines the key idea of maintenance of apps considering 9 vital factors for the estimation of maintainability utilizing an MCDM approach to evaluate the identified factors.

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What Makes a Great Mobile App? A Quantitative Study Using a New Mobile Crawler

Cited by 14 publications

References 12 publications

Smartphone App Usage Analysis: Datasets, Methods, and Applications

Smartphone App Usage Analysis: Datasets, Methods, and Applications

Quantification label for mobile apps

Mobile Phone Maintainability Prediction using MCDM Methodology

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