App store mining for iterative domain analysis: Combine app descriptions with user reviews. (22nd March 2019)

• Record Type: Journal Article
• Authors: Liu, Yuzhou; Liu, Lei; Liu, Huaxiao; Yin, Xinglong
• Is Part Of: Software, practice & experience. Volume 49:Number 6(2019)
• Page Start: 1013
• Page End: 1040
• Publication Date: 2019-03-22
• Subjects: app store mining -- domain analysis -- feature extraction -- reviews analysis; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2693
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: Compared with traditional software, the domain analysis of apps is conducted not only in the early stage of software development to gain knowledge of a particular domain but also runs throughout each iteration of apps to help developers understand evolution trends of the domain for maintaining their competitiveness. In this paper, we propose an approach to analyze app descriptions combined with reviews in App stores automatically and construct a feature‐based domain state model (FDSM) in the form of state machine to support the domain analysis of apps. In FDSM, the domain knowledge up to a certain moment together is defined as a state. Initial state summarizes the high‐level knowledge by gaining topics of app descriptions, whereas each transition is generated based on the information gained within one period of time and describes the change from the current state to the next one. Furthermore, user opinions in reviews are introduced into the model to quantify the value of information for helping developers get key domain knowledge efficiently. To validate the proposed approach, we conducted a series of experiments based on Google Play. The results show that FDSM can provide valuable information for supporting domain analysis, especially in the evolution process of apps.