  Numerous tools rely on automatic categorization of Android apps as part of
their methodology. However, incorrect categorization can lead to inaccurate
outcomes, such as a malware detector wrongly flagging a benign app as
malicious. One such example is the SlideIT Free Keyboard app, which has over
500000 downloads on Google Play. Despite being a "Keyboard" app, it is often
wrongly categorized alongside "Language" apps due to the app's description
focusing heavily on language support, resulting in incorrect analysis outcomes,
including mislabeling it as a potential malware when it is actually a benign
app. Hence, there is a need to improve the categorization of Android apps to
benefit all the tools relying on it. In this paper, we present a comprehensive
evaluation of existing Android app categorization approaches using our new
ground-truth dataset. Our evaluation demonstrates the notable superiority of
approaches that utilize app descriptions over those solely relying on data
extracted from the APK file, while also leaving space for potential improvement
in the former category. Thus, we propose two innovative approaches that
effectively outperform the performance of existing methods in both
description-based and APK-based methodologies. Finally, by employing our novel
description-based approach, we have successfully demonstrated that adopting a
higher-performing categorization method can significantly benefit tools reliant
on app categorization, leading to an improvement in their overall performance.
This highlights the significance of developing advanced and efficient app
categorization methodologies for improved results in software engineering
tasks.
