eprintid: 10057647
rev_number: 25
eprint_status: archive
userid: 608
dir: disk0/10/05/76/47
datestamp: 2018-10-05 12:09:27
lastmod: 2021-10-10 22:40:52
status_changed: 2018-10-05 12:09:27
type: proceedings_section
metadata_visibility: show
creators_name: Sarro, F
creators_name: Harman, M
creators_name: Jia, Y
creators_name: Zhang, Y
title: Customer Rating Reactions Can Be Predicted Purely Using App Features
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: App Store Analysis, Requirements Elicitation, App Features Extraction,, Rating Estimation, Mobile Applications, Software Analytics, Predictive Modelling,, Natural Language Processing, Machine Learning, Case Based Reasoning
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: In this paper we provide empirical evidence that the rating that an app attracts can be accurately predicted from the features it offers. Our results, based on an analysis of 11,537 apps from the Samsung Android and BlackBerry World app stores, indicate that the rating of 89% of these apps can be predicted with 100% accuracy. Our prediction model is built by using feature and rating information from the existing apps offered in the App Store and it yields highly accurate rating predictions, using only a few (11-12) existing apps for case-based prediction. These findings may have important implications for require- ments engineering in app stores: They indicate that app devel- opers may be able to obtain (very accurate) assessments of the customer reaction to their proposed feature sets (requirements), thereby providing new opportunities to support the requirements elicitation process for app developers.
date: 2018-10-15
date_type: published
publisher: IEEE
official_url: https://doi.org/10.1109/RE.2018.00018
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1589680
doi: 10.1109/RE.2018.00018
isbn_13: 978-1-5386-7418-5
lyricists_name: Harman, Mark
lyricists_name: Jia, Yue
lyricists_name: Sarro, Federica
lyricists_id: MHARM36
lyricists_id: YJIAX90
lyricists_id: FSSAR72
actors_name: Sarro, Federica
actors_id: FSSAR72
actors_role: owner
full_text_status: public
place_of_pub: Banff, Alberta, Canada
pagerange: 76-87
event_title: IEEE 26th International Requirements Engineering Conference, Banff, Alberta, Canada
event_location: Banff, Canada
event_dates: 20 August 2018 - 24 August 2018
institution: IEEE 26th International Requirements Engineering Conference (RE)
issn: 2332-6441
book_title: Proceedings of the IEEE 26th International Requirements Engineering Conference :RE 18
citation:        Sarro, F;    Harman, M;    Jia, Y;    Zhang, Y;      (2018)    Customer Rating Reactions Can Be Predicted Purely Using App Features.                     In:  Proceedings of the IEEE 26th International Requirements Engineering Conference :RE 18.  (pp. pp. 76-87).  IEEE: Banff, Alberta, Canada.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10057647/1/SarroRE18.pdf