?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Customer+Rating+Reactions+Can+Be+Predicted+Purely+Using+App+Features&rft.creator=Sarro%2C+F&rft.creator=Harman%2C+M&rft.creator=Jia%2C+Y&rft.creator=Zhang%2C+Y&rft.description=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%2C+based+on+an+analysis+of+11%2C537+apps+from+the+Samsung+Android+and+BlackBerry+World+app+stores%2C+indicate+that+the+rating+of+89%25+of+these+apps+can+be+predicted+with+100%25+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%2C+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%3A+They+indicate+that+app+devel-+opers+may+be+able+to+obtain+(very+accurate)+assessments+of+the+customer+reaction+to+their+proposed+feature+sets+(requirements)%2C+thereby+providing+new+opportunities+to+support+the+requirements+elicitation+process+for+app+developers.&rft.subject=App+Store+Analysis%2C+Requirements+Elicitation%2C+App+Features+Extraction%2C%2C+Rating+Estimation%2C+Mobile+Applications%2C+Software+Analytics%2C+Predictive+Modelling%2C%2C+Natural+Language+Processing%2C+Machine+Learning%2C+Case+Based+Reasoning&rft.publisher=IEEE&rft.date=2018-10-15&rft.type=Proceedings+paper&rft.publisher=IEEE+26th+International+Requirements+Engineering+Conference+(RE)&rft.language=eng&rft.source=+++++In%3A++Proceedings+of+the+IEEE+26th+International+Requirements+Engineering+Conference+%3ARE+18.++(pp.+pp.+76-87).++IEEE%3A+Banff%2C+Alberta%2C+Canada.+(2018)+++++&rft.format=text&rft.identifier=https%3A%2F%2Fdiscovery.ucl.ac.uk%2Fid%2Feprint%2F10057647%2F1%2FSarroRE18.pdf&rft.identifier=https%3A%2F%2Fdiscovery.ucl.ac.uk%2Fid%2Feprint%2F10057647%2F&rft.rights=open