Recommending New Features from Mobile App Descriptions

Abstract

The rapidly evolving mobile applications (apps) have brought great demand for developers to identify new features by inspecting the descriptions of similar apps and acquire missing features for their apps. Unfortunately, due to the huge number of apps, this manual process is time-consuming and unscalable. To help developers identify new features, we propose a new approach named SAFER. In this study, we first develop a tool to automatically extract features from app descriptions. Then, given an app, we leverage the topic model to identify its similar apps based on the extracted features and API names of apps. Finally, we design a feature recommendation algorithm to aggregate and recommend the features of identified similar apps to the specified app. Evaluated over a collection of 533 annotated features from 100 apps, SAFER achieves a Hit@15 score of up to 78.68% and outperforms the baseline approach KNN+ by 17.23% on average. In addition, we also compare SAFER against a typical technique of recommending features from user reviews, i.e., CLAP. Experimental results reveal that SAFER is superior to CLAP by 23.54% in terms of Hit@15.