  Classifying mobile apps based on their description is beneficial for several
purposes. However, many app descriptions do not reflect app functionalities,
whether accidentally or on purpose. Most importantly, these app classification
methods do not work if the app description is unavailable. This paper
investigates a Reverse Engineering-based Approach to Classify mobile apps using
The data that exists in the app, called REACT. To validate the proposed REACT
method, we use a large set of Android apps (24,652 apps in total). We also show
REACTs' extendibility for malware/anomaly detection and prove its reliability
and scalability. However, our analysis shows some limitations in REACT
procedure and implementation, especially for similar feature based app
grouping. We discuss the root cause of these failures, our key lessons learned,
and some future enhancement ideas. We also share our REACT tools and reproduced
datasets for the app market analyst, mobile app developers and software
engineering research communities for further research purposes.
