  Current app ranking and recommendation systems are mainly based on
user-generated information, e.g., number of downloads and ratings. However, new
apps often have few (or even no) user feedback, suffering from the classic
cold-start problem. How to quickly identify and then recommend new apps of high
quality is a challenging issue. Here, a fundamental requirement is the
capability to accurately measure an app's quality based on its inborn features,
rather than user-generated features. Since users obtain first-hand experience
of an app by interacting with its views, we speculate that the inborn features
are largely related to the visual quality of individual views in an app and the
ways the views switch to one another. In this work, we propose AppQ, a novel
app quality grading and recommendation system that extracts inborn features of
apps based on app source code. In particular, AppQ works in parallel to perform
code analysis to extract app-level features as well as dynamic analysis to
capture view-level layout hierarchy and the switching among views. Each app is
then expressed as an attributed view graph, which is converted into a vector
and fed to classifiers for recognizing its quality classes. Our evaluation with
an app dataset from Google Play reports that AppQ achieves the best performance
with accuracy of 85.0\%. This shows a lot of promise to warm-start app grading
and recommendation systems with AppQ.
