Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2018

Abstract

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

Discipline

Programming Languages and Compilers | Software Engineering

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2018 ACM Joint Meeting on European Software Engineering Con-ference and Symposium on the Foundations of Software Engineering (ESEC/SIGSOFT FSE 2018), Lake Buena Vista, FL, USA, 2018 November 4-9

First Page

904

Last Page

907

ISBN

9781450355735

Identifier

10.1145/3236024.3264595

Publisher

ACM, New York, USA

City or Country

Lake Buena Vista, FL, USA

Additional URL

https://doi.org/10.1145/3236024.3264595

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