INFAR: insight extraction from app reviews

• Main Authors: GAO, Cuiyun, ZENG, Jichuan, LO, David, LIN, Chin-Yew, LYU, Michael R., KING, Irwin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Programming Languages and Compilers; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4300; https://ink.library.smu.edu.sg/context/sis_research/article/5303/viewcontent/fse18demo_id10_p.pdf
• Institution: Singapore Management University

App reviews play an essential role for users to convey their feedback about using the app. The critical information contained in app reviews can assist app developers for maintaining and updating mobile apps. However, the noisy nature and large-quantity of daily generated app reviews make it difficult to understand essential information carried in app reviews. Several prior studies have proposed methods that can automatically classify or cluster user reviews into a few app topics (e.g., security). These methods usually act on a static collection of user reviews. However, due to the dynamic nature of user feedback (i.e., reviews keep coming as new users register or new app versions being released) and multiple analysis dimensions (e.g., review quantity and user rating), developers still need to spend substantial effort in extracting contrastive information that can only be teased out by comparing data from multiple time periods or analysis dimensions. This is needed to answer questions such as: what kind of issues users are experiencing most? is there an unexpected rise in a particular kind of issue? etc. To address this need, in this paper, we introduce INFAR, a tool that automatically extracts INsights From App Reviews across time periods and analysis dimensions, and presents them in natural language supported by an interactive chart. The insights INFAR extracts include several perspectives: (1) salient topics (i.e., issue topics with significantly lower ratings), (2) abnormal topics (i.e., issue topics that experience a rapid rise in volume during a time period), (3) correlations between two topics, and (4) causal factors to rating or review quantity changes. To evaluate our tool, we conduct an empirical evaluation by involving six popular apps and 12 industrial practitioners, and 92% (11/12) of them approve the practical usefulness of the insights summarized by INFAR.Demo Tool Website: https://remine-lab.github.io/paper/infar.html Demo Video: https://youtu.be/MjcoiyjA5TE