Mining non-functional requirements from App store reviews

User reviews obtained from mobile application (app) stores contain technical feedback that can be useful for app developers. Recent research has been focused on mining and categorizing such feedback into actionable software maintenance requests, such as bug reports and functional feature requests. However, little attention has been paid to extracting and synthesizing the Non-Functional Requirements (NFRs) expressed in these reviews. NFRs describe a set of high-level quality constraints that a software system should exhibit (e.g., security, performance, usability, and dependability). Meeting these requirements is a key factor for achieving user satisfaction, and ultimately, surviving in the app market. To bridge this gap, in this paper, we present a two-phase study aimed at mining NFRs from user reviews available on mobile app stores. In the first phase, we conduct a qualitative analysis using a dataset of 6,000 user reviews, sampled from a broad range of iOS app categories. Our results show that 40% of the reviews in our dataset signify at least one type of NFRs. The results also show that users in different app categories tend to raise different types of NFRs. In the second phase, we devise an optimized dictionary-based multi-label classification approach to automatically capture NFRs in user reviews. Evaluating the proposed approach over a dataset of 1,100 reviews, sampled from a set of iOS and Android apps, shows that it achieves an average precision of 70% (range [66% - 80%]) and average recall of 86% (range [69% - 98%]).