Automating app review response generation

Cuiyun Gao, Jichuan Zeng, Xin Xia, David Lo, Michael R. Lyu, Irwin King

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

6 Citations (Scopus)

Abstract

Previous studies showed that replying to a user review usually has a positive effect on the rating that is given by the user to the app. For example, Hassan et al. found that responding to a review increases the chances of a user updating their given rating by up to six times compared to not responding. To alleviate the labor burden in replying to the bulk of user reviews, developers usually adopt a template-based strategy where the templates can express appreciation for using the app or mention the company email address for users to follow up. However, reading a large number of user reviews every day is not an easy task for developers. Thus, there is a need for more automation to help developers respond to user reviews. Addressing the aforementioned need, in this work we propose a novel approach RRGen that automatically generates review responses by learning knowledge relations between reviews and their responses. RRGen explicitly incorporates review attributes, such as user rating and review length, and learns the relations between reviews and corresponding responses in a supervised way from the available training data. Experiments on 58 apps and 309,246 review-response pairs highlight that RRGen outperforms the baselines by at least 67.4% in terms of BLEU-4 (an accuracy measure that is widely used to evaluate dialogue response generation systems). Qualitative analysis also confirms the effectiveness of RRGen in generating relevant and accurate responses.

Original languageEnglish
Title of host publicationProceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
EditorsJulia Lawall, Darko Marinov
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages163-175
Number of pages13
ISBN (Electronic)9781728125084
ISBN (Print)9781728125091
DOIs
Publication statusPublished - 2019
EventAutomated Software Engineering Conference 2019 - San Diego, United States of America
Duration: 10 Nov 201915 Nov 2019
Conference number: 34th
https://2019.ase-conferences.org/ (Conference website)
https://dl.acm.org/doi/proceedings/10.5555/3382508 (Proceedings)

Conference

ConferenceAutomated Software Engineering Conference 2019
Abbreviated titleASE 2019
Country/TerritoryUnited States of America
CitySan Diego
Period10/11/1915/11/19
Internet address

Keywords

  • App reviews
  • Neural machine translation
  • Response generation

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