Analyzing reviews guided by App descriptions for the software development and evolution. Issue 12 (11th October 2018) - Liu, Yuzhou; Liu, Lei; Liu, Huaxiao; Wang, Xiaoyu
Abstract: Reviews in App stores are a massive and fast‐growing data resource for developers to understand user experiences and their needs. Studies show that users often express their sentiments on App features in reviews, and this information is important for the development and evolution of Apps. To help developers gain such information efficiently, this paper proposes a method using App descriptions, another typical data in App stores, to guide the analysis of reviews. Firstly, we extract App features from descriptions, then summarize them to gain topics of App features as high‐level information; the results are formalized as a topic‐based domain model (TBDM). Secondly, we train classifiers of reviews based on the model to establish the relationships between user sentiments and App features. Finally, a quantified method is given to analyze the model based on developer preferences for recommending and summarizing reviews. To evaluate our approach, experiments were conducted using the App descriptions and reviews collected from Google Play. The results indicate that the approach can classify reviews to their related App features effectively (average F measure is 86.13%), and provides useful information for overall analyzing App features in a domain and identifying (dis)advantages of an App. Abstract : This paper introduces App descriptions into the process of review analysis for deeply mining user sentiments on App features. In our approach, we establish a topic‐based domain model with reviews (R‐TBDM) to formally describe the information gained from App descriptions and reviews. Meanwhile, the demands of developers are defined as developers' preferences and taken as the basis to analyze the model quantitatively, so that we can provide valuable information for supporting the development and evolution of Apps effectively.