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Recent Awards

Kai Lukoff

Kai Lukoff

Kai Lukoff with the Computer Science and Engineering Department has received a $174,555 award from the National Science Foundation.

Kai Lukoff with the Computer Science and Engineering Department has received a $174,555 award from the National Science Foundation to support his project "The Systematic Mobile App Review (SMAR) Research Method - A Tool and Guidelines for Understanding the Mobile App Ecosystem."

Kai Lukoff with the Computer Science and Engineering Department has received a $174,555 award from the National Science Foundation to support his project "The Systematic Mobile App Review (SMAR) Research Method - A Tool and Guidelines for Understanding the Mobile App Ecosystem."

Whereas well-established best practices exist for systematic literature reviews (e.g., PRISMA reporting standards), best practices for systematic app reviews (SARs) are unclear and the process of data collection from mobile app stores is costly and challenging for researchers. The goal of this project is to develop best practices and open-source tools that enable even non-technical researchers to easily conduct systematic app reviews. A SAR collects and analyzes data from mobile app stores (e.g., Google Play) to gain a systematic understanding of the state of the mobile app ecosystem and/or the features and practices within apps in a domain such as mental health or privacy. For example, a SAR by psychology researchers might examine adherence to clinical guidelines in apps that contain the keyword "cognitive behavioral therapy" in the description and have been installed over a million times. A SAR by privacy researchers might use an automated analysis of application packages to check for the prevalence of third-party trackers within children's apps, where targeted advertising is illegal in the U.S. and EU. SARs can provide an empirical understanding of the app ecosystem in a specific domain, inform regulatory policy, and/or inspire ideas for new mobile apps. In this research project, we will systematically review the literature to understand existing goals and practices for SARs (phase one), provide personalized technical consulting for researchers from different domains who wish to conduct SARs (phase two), and finally develop best practices and self-service open source tools to enable any researcher to conduct a SAR on their own (phase 3).