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Despite continuous efforts to build and update network infrastructure, mobile devices in developing regions continue to be constrained by limited bandwidth. Unfortunately, this coincides with a period of unprecedented growth in the size of…
"Incentivized" advertising platforms allow mobile app developers to acquire new users by directly paying users to install and engage with mobile apps (e.g., create an account, make in-app purchases). Incentivized installs are banned by the…
Disaster mobile apps play an increasingly important role in disseminating hazard information and supporting communities during emergency situations. This study presents a comprehensive analysis of these mobile applications, focusing on…
With the continuous growth in the usage of Android apps, ensuring their security has become critically important. An increasing number of malicious apps adopt anti-analysis techniques to evade security measures. Although some research has…
Online app search optimization (ASO) platforms that provide bulk installs and fake reviews for paying app developers in order to fraudulently boost their search rank in app stores, were shown to employ diverse and complex strategies that…
Automatic testing of mobile applications has been a well-researched area in recent years. However, testing in industry is still a very manual practice, as research results have not been fully transferred and adopted. Considering mobile…
We report the findings of a reimplementation of 18 foundational studies in feature-based machine learning for Android malware detection, published during the period 2013-2023. These studies are reevaluated on a level playing field using a…
Intelligent Apps (iApps), equipped with in-App deep learning (DL) models, are emerging to offer stable DL inference services. However, App marketplaces have trouble auto testing iApps because the in-App model is black-box and couples with…
Many users make quick decisions that affect their data privacy without due consideration of their values. One such decision is whether to download a smartphone app to their device. Previous work has suggested a relationship between values,…
User engagement is crucial to the long-term success of a mobile app. Several metrics, such as dwell time, have been used for measuring user engagement. However, how to effectively predict user engagement in the context of mobile apps is…
As artificial intelligence rapidly transforms society, developers and policymakers struggle to anticipate which applications will face public moral resistance. We propose that these judgments are not idiosyncratic but systematic and…
Many apps have basic accessibility issues, like missing labels or low contrast. Automated tools can help app developers catch basic issues, but can be laborious or require writing dedicated tests. We propose a system, motivated by a…
The deep learning approach to detecting malicious software (malware) is promising but has yet to tackle the problem of dataset shift, namely that the joint distribution of examples and their labels associated with the test set is different…
Android is the most popular mobile operating system in the world, running on more than 70% of mobile devices. This implies a gigantic and very competitive market for Android apps. Being successful in such a market is far from trivial and…
Dating apps for mobile devices, one popular GeoSocial app category, are growing increasingly popular. These apps encourage the sharing of more personal information than conventional social media apps, including continuous location data.…
Android-based smart devices are exponentially growing, and due to the ubiquity of the Internet, these devices are globally connected to the different devices/networks. Its popularity, attractive features, and mobility make malware creator…
Lead user driven innovation and open innovation paradigms seek to involve consumers and common people to innovative product development projects. In order to help developers choose ideas that meet the end users' needs, we undertook a…
Listening to user's requirements is crucial to building and maintaining high quality software. Online software user feedback has been shown to contain large amounts of information useful to requirements engineering (RE). Previous studies…
Predictive models are increasingly used to make various consequential decisions in high-stakes domains such as healthcare, finance, and policy. It becomes critical to ensure that these models make accurate predictions, are robust to shifts…
The ubiquity of computing devices has led to an increased need to ensure not only that the applications deployed on them are correct with respect to their specifications, but also that the devices are used in an appropriate manner,…