46,135 research outputs found
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
Regulating Mobile Mental Health Apps
Mobile medical apps (MMAs) are a fastâgrowing category of software typically installed on personal smartphones and wearable devices. A subset of MMAs are aimed at helping consumers identify mental states and/or mental illnesses. Although this is a fledgling domain, there are already enough extant mental health MMAs both to suggest a typology and to detail some of the regulatory issues they pose. As to the former, the current generation of apps includes those that facilitate selfâassessment or selfâhelp, connect patients with online support groups, connect patients with therapists, or predict mental health issues. Regulatory concerns with these apps include their quality, safety, and data protection. Unfortunately, the regulatory frameworks that apply have failed to provide coherent riskâassessment models. As a result, prudent providers will need to progress with caution when it comes to recommending apps to patients or relying on appâgenerated data to guide treatment
Mining Mobile Youth Cultures
In this short paper we discuss our work on coresearch devices with a young coder community, which help investigate big social data collected by mobile phones. The development was accompanied by focus groups and interviews on privacy attitudes, and aims to explore how youth cultures are tracked in mobile phone data
User Review-Based Change File Localization for Mobile Applications
In the current mobile app development, novel and emerging DevOps practices
(e.g., Continuous Delivery, Integration, and user feedback analysis) and tools
are becoming more widespread. For instance, the integration of user feedback
(provided in the form of user reviews) in the software release cycle represents
a valuable asset for the maintenance and evolution of mobile apps. To fully
make use of these assets, it is highly desirable for developers to establish
semantic links between the user reviews and the software artefacts to be
changed (e.g., source code and documentation), and thus to localize the
potential files to change for addressing the user feedback. In this paper, we
propose RISING (Review Integration via claSsification, clusterIng, and
linkiNG), an automated approach to support the continuous integration of user
feedback via classification, clustering, and linking of user reviews. RISING
leverages domain-specific constraint information and semi-supervised learning
to group user reviews into multiple fine-grained clusters concerning similar
users' requests. Then, by combining the textual information from both commit
messages and source code, it automatically localizes potential change files to
accommodate the users' requests. Our empirical studies demonstrate that the
proposed approach outperforms the state-of-the-art baseline work in terms of
clustering and localization accuracy, and thus produces more reliable results.Comment: 15 pages, 3 figures, 8 table
Automatically combining static malware detection techniques
Malware detection techniques come in many different flavors, and cover different effectiveness and efficiency trade-offs. This paper evaluates a number of machine learning techniques to combine multiple static Android malware detection techniques using automatically constructed decision trees. We identify the best methods to construct the trees. We demonstrate that those trees classify sample apps better and faster than individual techniques alone
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