Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2022

Abstract

User experience of mobile apps is an essential ingredient that can influence the user base and app revenue. To ensure good user experience and assist app development, several prior studies resort to analysis of app reviews, a type of repository that directly reflects user opinions about the apps. Accurately responding to the app reviews is one of the ways to relieve user concerns and thus improve user experience. However, the response quality of the existing method relies on the pre-extracted features from other tools, including manually labelled keywords and predicted review sentiment, which may hinder the generalizability and flexibility of the method. In this article, we propose a novel neural network approach, named CoRe, with the contextual knowledge naturally incorporated and without involving external tools. Specifically, CoRe integrates two types of contextual knowledge in the training corpus, including official app descriptions from app store and responses of the retrieved semantically similar reviews, for enhancing the relevance and accuracy of the generated review responses. Experiments on practical review data show that CoRe can outperform the state-of-the-art method by 12.36% in terms of BLEU-4, an accuracy metric that is widely used to evaluate text generation systems.

Keywords

User reviews, retrieved responses, app descriptions, pointer-generator network

Discipline

Databases and Information Systems | OS and Networks | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

ACM Transactions on Software Engineering and Methodology

Volume

31

Issue

1

First Page

1

Last Page

36

ISSN

1049-331X

Identifier

10.1145/3464969

Publisher

Association for Computing Machinery (ACM)

Additional URL

https://doi.org/10.1145/3464969

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