Learning features that predict developer responses for iOS app store reviews

Abstract

Background: Which aspects of an iOS App Store user review motivate developers to respond? Numerous studies have been conducted to extract useful information from reviews, but limited effort has been expended to answer this question. Aims: This work aims to investigate the potential of using a machine learning algorithm and the features that can be extracted from user reviews to model developers' response behavior. Through this process, we want to uncover the learned relationship between these features and developer responses. Method: For our prediction, we run a random forest algorithm over the derived features. We then perform a feature importance analysis to understand the relative importance of each individual feature and groups thereof. Results: Through a case study of eight popular apps, we show patterns in developers' response behavior. Our results demonstrate not only that rating and review length are among the most important but also that review posted time, sentiment, and the writing style play an important role in the response prediction. Additionally, the variation in feature importance ranking implies that different app developers use different feature weights when prioritizing responses. Conclusions: Our results may provide guidance for those building review or response prioritization tools and developers wishing to prioritize their responses effectively.